Towards the Unknown: Flanders, Svensson, McGann

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Link to Prezi:http://prezi.com/9j0jqispl6vg/digital-humanities-presentation/

I’ve entitled my presentation Towards the Unknown, as all three of readings for today are in some way about moving beyond what is comfortable and familiar, and taking a critical stance in relation to the use of technology in the digital humanities. Flanders invokes the unknown in her idea of “productive unease,” in which the friction produced by the unfamiliarity of digital representation makes researchers look more closely both at the object being represented and at representation itself. The unknown also appears in Svensson’s call for a uniquely humanistic cyberinfrastructure that doesn’t rely uncritically on familiar models from the sciences or the humanities, and, of course, in McGann’s notion of imagining what we do not know. The unknown will serve as a thread throughout my discussion of each of these articles and their emphasis on the unfamiliar in the constructions they offer for the digital humanities.

In “The Productive Unease of 21st-Century Digital Scholarship,” Julia Flanders starts with the model of technological progressivism, the idea that technology is getting better and better, and is doing so faster and faster. She argues that digital humanities often adopts a similar sense of progress, in that technological developments make more digital humanities projects possible. This narrative, however, works only for the “digital” side of digital humanities: while humanities scholarship may want to believe that each generation of critics is doing better work than the one that came before, it also resists a self-narrative of inevitable progress, instead privileging development and self-reflection. Flanders argues that rather than being concerned with the next big technological development, digital humanities should instead think critically about its use of technology.

Flanders points to representational technologies such as XML as not just representing textual objects, but also producing a distancing effect through their unfamiliarity that allows us to see the original more clearly. Flanders calls this effect “productive unease,” “a sense of friction” between accustomed ways of thinking and the new tool, between the familiar and the unknown. In Flanders’s formulation, this friction is key: projects that lack this tension may produce knowledge, but they will not produce something meaningfully new, because they lack the spark that can lead to truly innovative ways of thinking.

Flanders identifies three areas where digital humanities work is producing interesting critical friction:

  1. Digital scholarship is uneasy about the significance of medium. The digital medium functions as “a kind of meta-medium in which other media can be modeled or represented,” foregrounding the representational strategies of particular media.
  2. Digital scholarship is uneasy about the institutional structures of scholarly communication. Digital scholarship’s hybrid position, in close relation to the academy but not entirely at home in it, highlights the arbitrary limits of academic institutions.
  3. Digital scholarship is uneasy about the significance of representation in forming models of the world. In particular, Big Data models raise the issue of how to adapt scholarly methods developed to focus on precision and craft to analyzing massive data sets.

Flanders concludes by offering her own model for humanities scholarship: that it is not a forward motion, but a dialectic, whose success is measured not in power or speed but in the degree to which it makes us think, opening up the unknown to us in a useful way.

Patrik Svensson’s article “From Optical Fiber to Conceptual Cyberinfrastructure” displays his concern with developing a humanities-specific infrastructure. (While “cyberinfrastructure” is in the title, the kinds of structures Svensson discusses are not limited purely to the digital realm, but instead demonstrate his interest in the larger systems in which the cybernetic or digital components are embedded.) Svensson notes that conversations about infrastructure have so far been dominated by the sciences, which limits what is considered infrastructure, how that infrastructure is constructed, and what uses that infrastructure allows, particularly in a humanities context. The focus on science also drives institutional priorities and funding towards particular types of projects, in which the humanities rarely play a major part. For cyberinfrastructure in particular, the emphasis on science has resulted in great value being placed on having the most up-to-date technology possible. Svensson argues that the humanities have different needs and concerns surrounding cyberinfrastructure that need to be part of the conversation, and that humanities scholars need to think carefully and critically about what those needs are now and might be in future.

In relation to this thought process, Svensson identifies two flawed views about current humanities infrastructure that have the potential to distort future developments. One is the idea that digitizing the existing, library-based humanities infrastructure is sufficient to move forward. Svensson argues that this is an overly limited approach which does not allow for the emergence of other models, and which fails to take into account the changing nature of libraries themselves. The second flawed view is that the humanities lack infrastructure entirely. Aside from being simply false (or at least assuming an extremely narrow view of what constitutes infrastructure), presenting the humanities as a blank slate renders adopting the science model of infrastructure more tempting, which, again, would limit the kinds of humanities projects such an infrastructure could support. Svensson argues that humanities infrastructure needs to embrace the unknown: rather than uncritically adopting the existing, familiar library-based or science-based models, to develop something new.

To illustrate how a new humanities infrastructure might be implemented, Svensson offers as a case study the HUMlab at Umea University in Sweden, for which he is the director. He describes the HUMlab as a “collaborative and intersectional place,” and its place-ness is key to the programs it supports. The space is configured not just for function, but also to promote an environment in keeping with its intellectual principles as manifest in design. The HUMlab’s design principles are a key layer between conceptual cyberinfrastructure and its actual implementation, forming a bridge from the abstract to the concrete.

Based on his experience with HUMlab, Svensson offers some general strategies for implementing humanities cyberinfrastructure. These include viewing infrastructure systematically, maintaining regard for physical space and local context, taking a step back from the cutting edge to find what works for a particular set of circumstances, staying involved in low-level technical decisions that may have a disproportionate impact on the finished product, and taking a vocal role in the decision-making process.

Jerome McGann, in “Imagining What You Don’t Know: The Theoretical Goals of the Rossetti Archive,” discusses some of the possibilities that digital technology has opened up for textual editing. McGann identifies several benefits of the digital approach. One is being able to combine multiple critical approaches in one edition. Another is the opportunity to take a crucial step back: rather than analyzing a text through adding a layer of textual criticism to the original, analyzing a text through digital tools introduces another level of critical abstraction, which McGann compares to analyzing natural phenomena through mathematics. Another benefit is the option to build the history of the project into the project itself.

McGann’s digital edition of Dante Gabriel Rossetti’s textual and artistic work, The Rossetti Archive, serves as a demonstration of some of the issues entailed in digital textual criticism. McGann describes the history of the project’s development. In the project’s early stages, two imaginative protocols for text and database management were common: a visual approach, focusing on information browsing and coded in HTML, and a conceptual approach, focusing on database analysis and coded in SGML. The Rossetti Archive straddled the boundary in being designed to privilege both approaches equally.

McGann describes The Rossetti Archive as a tool for “imagining what you don’t know,” where the “you” in his formulation is the project developers. In creating the archive, the challenges of making complex texts and images encodable prompted McGann and his collaborators to think about the materials in a different way (and, ultimately, to devise new tools they hadn’t initially planned, such as Inote). This act of imagination, however, is situated in the act of conceptualizing the archive for representation in the project, not in the ongoing use of the resultant representations by researchers.

McGann ends his article by describing an experiment of running through random digital image deformations of Rossetti’s painting The Blessed Damozel, an exploration that echoes Julia Flanders’s point about how technological approaches can have a distancing effect that leads to seeing an object anew. While I’m not entirely convinced that this experiment leads to any deep insights about The Blessed Damozel that could not be otherwise achieved, it does point to the importance of making room for play and experimentation as another way of imagining the unknown. But, again, this imaginative act is not offered to researchers; there is no tool built into The Rossetti Archive to enable researchers to create their own deformations of the images contained therein.

Reading these articles together raises several questions about the design and implementation of digital humanities projects.

  1. Do the HUMlab and The Rossetti Archive display the “productive unease” Flanders identifies as essential to innovative digital humanities projects? Why or why not?
  2. If, as Svensson argues, the library-based model of humanities infrastructure is overly limited, what other models might future humanities infrastructure projects pursue?
  3. To what extent does your grant proposal project move towards the unknown and take a critical stance towards it use of technology?

For my Prezi, I was broadly inspired by Svensson’s notion of the underground as a productive space for experimentation and by Flanders’s attention to the slippage between representation and object. The overall composition and movement of my canvas was to move downwards and then dig in deeper. My use of images as much as possible, rather than text, was aimed at invoking, again very broadly, a kind of “productive unease” in the collision between the ideas conveyed verbally and the images displayed, each of which suggested its own array of meanings beyond the representational uses for which I was appropriating it.

Defining Digital Humanities (again)

Link to my Prezi: http://prezi.com/7y4quke4n1hp/defining-digital-humanities-some-more/

Before we get started defining “Digital Humanities,” let us begin with the humanities themselves. Why are they worth digi-fying? Ian Bogost, a Literature professor at Georgia Tech and video game studio co-founder, offers us several reasons. He first cites David Palumbo-Liu, a Stanford professor, who claims that,

“Lowering the bar for the humanities, or even dismissing the humanities as not having anything specific to teach us, is not only abrogating our responsibilities as teachers, but also ignoring the very patent evidence that the humanities are our solace and aid in life, and we need them now more than ever.”

Bogost concludes from this that “Humanities is indispensable […] because some people still find insight in novels.” This is unsatisfactory as both reasoning and conclusion, and so Bogost moves on to Immanuel Kant. Kant discusses two disparate “faculties” of man – the lower faculties, which regard theoretical reasoning, and the higher faculties, which regard practical reasoning. Personally I find it interesting that Kant believes theoretical reasoning to be “lower” – most academics I know would certainly rate theory above practicality in any forum – but in any case, Kant believes the two sorts of faculties to be entirely separate from one another. Interestingly, this view influenced the design of the University of Berlin, which influenced modern universities, which is why we have a “School of Law,” a “School of Medicine,” a “School of Business” and “Information,” and then separate “majors” in the liberal arts and sciences.

Bogost argues that there is a gap between the two types of faculties into which those attempting to justify the humanities typically fall. Humanists want to belong to both legs; they want to lay claim to the erudition of the lower faculties, while still maintaining a real-world applicability and utility. Unfortunately, as Bogost notes (and I agree), these “uses” which Humanists tout – “Critical thinking,” “lifelong learning,” “cultural perspectives,” etc. – are not only relatively useless, but they are also easily attainable through many other fields of study.

Having reached this somewhat depressing conclusion, Bogost tries another Humanities defense on for size; that of Stanley Fish, New York Times opinion writer. Stanley writes,

“To the question “of what use are the humanities?”, the only honest answer is none whatsoever. And it is an answer that brings honor to its subject. Justification, after all, confers value on an activity from a perspective outside its performance. An activity that cannot be justified is an activity that refuses to regard itself as instrumental to some larger good. The humanities are their own good.”

Although Bogost does not seem particularly impressed with this stark refusal to rationalize studying the humanities, I actually kind of like it. I think it is a defense which is frequently used when referring to the visual and performing arts – they exist simply to improve the lives of those who enjoy them. While there is no denying it’s a recursive definition, I very much like that, as Bogost says, “It was definitive. It held the line” – it is refreshingly un-wishy-washy.

Nevertheless, Bogost moves on, searching for a rationale which allows the humanities to bridge the faculty gap, rather than fall into it. He does not think there is anything wrong with being “useful,” and that the true disconnect between humanities and the “real world” lies with the humanists themselves, not with the subject matter. He describes humanists as “vampires who can’t remember the warmth of daylight” who “bear active disdain for actual humans, whom they often perceive to be ignorant suckers.”

Humanists are closeted right at the top of the ivory tower, and in order to make any sort of justification for studying the humanities, they must walk down the stairs and greet the rest of humanity. We must also, besides simply meeting with and walking among the crowd, become a part of it and begin to explore its intricacies and intrigues. Bogost cites Stephen Palmquist, who urges us to become “the general public’s spy,” and takes his reasoning even a step further, arguing essentially that we (as humanists), must become agoratropic, turn our faces toward the crowd rather than staring endlessly into souls just like our own.

Bogost claims that humanists rarely “see themselves as practitioners of daily life” (emphasis Bogost’s), and that that is absolutely necessary for the continued study of the humanities.

Once Bogost finishes defining and arguing for change in the humanities, he sets out to discuss the “digital” aspect. He first cites the “Digital Humanities Manifesto 2.0,” which defines “Digital Humanities” as “an array of convergent practices” surrounding both the transition from print to “multimedia” and the use of new digital tools in the arts and humanities.” Bogost does not fully embrace this definition, but he also declines to provide one of his own, deciding that too many exist already, and instead decides to inspect Patrick Svensson’s model, which melds the various definitions into a few different types of categories (tool, object of study, expressive medium, laboratory, activist venue).

Bogost dismisses most of these out-of-hand, citing their “speculative natures” or the fact that they don’t all cover every aspect of humanities computing. One category Bogost does latch on to however is “tool.” As we know from experience, the Digital Humanities are full of tools; some make our lives easier, and some make them much, much more difficult. Everyone seems to have jumped on the “tool bandwagon” – as we discussed in class, some who would define “Digital Humanities” explicitly exclude non-tool-creators from their ranks.

Bogost disagrees with this pattern, and encourages instead thinking of “Computational Humanities” rather than “Digital Humanities.” This presents an interesting semantic twist. As Bogost points out, the Humanities have traditionally been very reluctant to embrace anything relating remotely to computers and computer science (possibly for fear of being obsolesced?), even while the hard sciences embrace the “computational” modifier.

I like and support Bogost’s suggestion. I think “Computational” implies progress, inquiry, speed; as Bogost states, “Computational methods tend to emphasize information processing and analysis over the creation and dissemination of information assets.” We are not simply digitizing a collection and making it available online. No. If we are “computing,” we are enabling new research, discovering new trends and data, and contributing to the world’s canon of knowledge.

Bogost goes so far as to claim that we (again, as humanists) ought to be embarrassed at the fact that “digital tools for creation, collaboration, and dissemination do indeed represent a significant change,” and that “making it customary” to use digital tools should “be seen as just the tiniest baby step in reclaiming a lost worldly responsibility,” rather than one of our end goals, as it seems to be now.

So, how will we do that then? Bogost provides a few suggestions. First and foremost is to catch up with the rest of the academic world, technology-wise. Bogost takes us through the 80s, 90s, and 2000s, when “everyone (save the humanists)” was introduced to the personal computer, the web, digitization, and a variety of other techy tool sets, meanwhile the humanists have just signed up for “Twitter,” and we can’t get enough of ourselves.

Secondly, we need to stop acquiring every tool we encounter and trying somehow to fit them into our projects with no concern for their relevance or actual utility. If we want to fully make use of digital tools, we need to either begin crafting our own project-specific ones, or significantly modify existing tools to truly fit our purposes. When we simply accept what is given to us, we are “reversing the intended purpose of the humanities as public spies: taking whatever works from the outside word un-or under-questioned.” If humanists want to stay relevant and begin really inspiring new research, they are going to have to embrace computer science.

While there was some dissent about this in class (whether a digital humanist really needed coding experience), I firmly believe it is the case. Without a larger presence of strong coders, and a much larger presence of humanists with a basic familiarity with coding, humanities is indeed doomed to obsolescence. As long as humanists continue to rely on others to create and manage their toolsets, they will be at a disadvantage when it comes to innovation and conclusion-drawing.

Humanists also, according to Bogost, need to worry less about being useful. I find it somewhat amusing that that even needs to be said, but being well acquainted with quite a few “academics,” I am not all that surprised. Basically, we need to take Palmquist’s “spy” image, update that spy’s technology by about twenty years, and we have a much better picture of what Bogost thinks a “Computational Humanist” ought to look like.

Finally, Bogost again pleads with humanists to “plant forests” rather than “tend potted plants.” He argues that humanist work needs to have larger meaning and relevance, and I agree, although I see a much different metaphor. On the one hand, we have “traditional” humanists (represented by Saruman) who would molder away in their towers, grasping at the last straws of the old paper age, fighting change, and being generally unloved. On the other hand, we have the “humanists of the future” (represented by Gandalf) who work toward world enlightenment and harmony, and both accept and even look for help from all sorts of unexpected places.

With that, Bogost signs off, leaving us to contemplate our altered view of “Digital Humanities,” and to attempt to apply that view as we encounter future examples of “Digital” or “Computational” humanities.

One such project is the Scaife Digital Library. Christopher Blackwell, an Associate Classics professor at Furman University, along with Greg Crane, professor of Classics at Tufts and head of the Perseus project, describe this project in great detail in their article on Cyberinfrastructure and Classics in the “Digital Age.”

This article started out very well. The authors laud Scaife for being

“Among the first to recognize the importance of making our publications fully open — it is not enough to provide a single perspective via a single web site with primary and secondary sources. We need to make the source materials accessible — others need to be able to download what we produce, apply their own analytical methods, and even build new derivative works on what others have done.” (4)

This seems very much in line with Bogost’s move towards computation over digitization; Scaife wanted his project to further new research, new learning; not simply provide online access. Great, sounds good so far.

Next, the authors discuss something called a “Memography,” which is essentially the entire corpus of work centered on one figure or event. We all know what a meme is today, thanks to lolcats and viral videos, but historically, the word “meme” comes from the Greek word “mimesis,” which means “imitation.” Crane and Blackwell here cite the myth of Achilles’ choice as an example of the first real meme: Achilles must decide whether he wants to die young, and have his name remembered forever, or live a long life on earth and be forgotten immediately. He of course chooses everlasting fame, and so becomes a cultural meme.

The goal of the Scaife Digital Library is to create a “Cyberinfrastructure” which can support all the various languages, document types, media types, etc. which go into creating a memography. A cyberinfrastructure is actually more self-explanatory than you might think, despite the multitude of contradictory Google images which come up when you search it. Wikipedia defines “cyberinfrastructures” as:

“Research environments that support advanced data acquisition, data storage, data management, data integration, data mining, data visualization and other computing and information processing services distributed over the Internet beyond the scope of a single institution. In scientific usage, cyberinfrastructure is a technological and sociological solution to the problem of efficiently connecting laboratories, data, computers, and people with the goal of enabling derivation of novel scientific theories and knowledge.”

Essentially, a cyberinfrastructure is a giant fancy box that is capable of storing, managing, and visualizing data across multiple institutions. So, Scaife’s goal is to create one, and he advocates using Classics to do it first. The authors of this paper list a great many reasons why Classics ought to be the first area of study to be “cyberinfrastructured,” among which is Classics’ multicultural-ness, its history and staying-power, its archaeological record, the fact that scholars have already started working with it, and that it is super awesome (my own reasoning).

All of this is fabulous, and seems like it will support Bogost’s desire for practicality and computational possibilities, at least, until we get to the actual list of tools: Canonical Text Services (CTS), Optical Character Recognition and Page Layout Analysis, Morphological Analysis, Syntactic Analysis, Word Sense Discovery, Named Entity Identification, Metrical Analysis, Translation Support, Cross Language Information Retrieval (CLIR), Citation Identification, Quote Identification, Translation Identification, Version Analysis, Text Alignment, and Markup Projection. Just reading all that makes me tired! More importantly, all of these are “tools” which simply increase readability, and while they might facilitate research for other Classicists, they are not making any impact on the real world. If someone is not already a Classical scholar, he or she is not going to appreciate any of the digitization efforts of this project.

Saddened by this dip into ivory-tower-mentality, I read on, only to discover Blackwell and Crane touting even more text-based digital tools. WordNets and Machine-Actionable Dictionaries, Treebanks, Linguistic Annotations, and Machine-Actionable Grammars, Machine-Actionable Indices of people, places, organizations, etc., Multitexts, Parallel Texts, Propositional Knowledge, and Commentaries. While all of these things might be slightly useful, the amount of work which must go into making them possible will certainly not be recouped in the amount of use they receive. All these “helpful tools” remind me of our discussion on whether coding XML actually provides information to anyone but the person who did the coding. Bogost’s call for humanists to turn their faces toward the common people is exactly what this paper and project seem to be missing.

That is, until the bottom, when the authors (or Thucydides, rather) reassure us they are aware of actual humanity:

“An Athenian citizen does not neglect the state because he takes care of his own household; and even those of us who are engaged in business have a very fair idea of politics. We alone regard a man who takes no interest in public affairs, not as a harmless, but as a useless character; and if few of us are originators, we are all sound judges of a policy. The great impediment to action is, in our opinion, not discussion, but the want of that knowledge which is gained by discussion preparatory to action.  (Thuc. 2.40.2, after Crawley)”

The authors continue to follow this thread of ideas, pointing out that in an age of digital publishing, scholarship is more widely available than ever before, and the “barriers for entry” into Academia are much lower than they were before. As Blackwell and Crane conclude, “The digital medium offers new methods with which to make Greco-Roman culture and classical Greek and Latin physically and intellectually accessible to audiences vastly larger and more diverse than was ever feasible in print,” and that is a good thing.

Discussion Questions:

1. Do the Humanities need to be useful to be justifiably studied?

2. What do you think of “Computational Humanities” vs. “Digital Humanities”?

3. Do you think the Scaife Digital Library goes far enough toward Bogost’s “computational” ideal? Or is it stuck in the “digital” age?

Prezi Notes:

If you have ever watched a presentation given by a computer scientist, you might have noticed they almost exclusively use white slides with basic black text. While this is not the most visually stimulating, it is extremely effective at conveying information in a clear, concise way. I chose to reference that style since my presentation discussed in great part the relationship between the fields of Computer Science and Humanities.

I am also a very visual learner, and I feel that pictures (especially funny ones) help people remember points a good deal better than lots of text. Using mainly visuals also allows the presenter more freedom when speaking, and it keeps the viewers’ minds interested and attentive.

On Publication: The Good, the Bad, and the Ugly

The link to my Prezi: http://prezi.com/dfpaxbfejzk8/on-publication/

Worse than Rupert Murdoch?

In George Monbiot’s article in The Guardian, a British newspaper, entitled “Academic publishers make Murdoch look like a socialist,” he states that academic publishers are the most ruthless capitalists in the Western world.  He compares academic journals to Murdoch’s paywall for the Times of London, which charges one pound for 24 hours of access to the website.  In that time, however, the user can read and download as many articles as they would like.  With academic journals, the user must pay per article.  He cites Wily-Blackwell which charges $42 for access to one article, no matter it’s publishing date.

There is the option of going to the library to access the article, but the average price of an annual subscription is around $4000.  The most expensive subscription fee Monbiot could find was $20,390 for Elsevier’s Biochimica et Biophysica Acta.  Journals now account for 65% of academic libraries annual budget, which means that they have to buy fewer books to make room for the journals.

He points out that at least Murdoch pays his journalists and editors, and most of the content on his sites is self-generated.  With academic publishing, the articles, peer reviews, and editors come to them for free, and the material they publish was commissioned and funded by government research grants and academic stipends, which we pay for.  To see it, we must pay for it again.

Monopoly isn’t just a game

The profit they make is quite high.  Elsevier’s profit margin was 36% last year.  Elsevier, Springer, and Wiley publish 42% of all journal articles.  Universities are forced to buy these journals, as academic papers are only published in one place and researchers must read them to stay up-to-date in their field.  There is no competition because papers can only be published in one journal.  In many cases, the publishers cajole the libraries into buying many different journals even if they’re only interested in one or two.

The publishers claim they must charge these fees because the costs of production and distribution are so high, and their brand name adds value to the research.  An analysis by Deutsche Bank, however, found that publishers add little value to publishing process and that if it were as costly and complex as they claim, it would be impossible for them to be making a 40% profit.  In addition, the long turnaround time many journals have can delay the release of finding by a year or more, doing very little to assist the dissemination of research.

Monbiot claims this is pure capitalism, “monopolizing a public resource than charging exorbitant fees to use it.”  This issue is bad enough for universities, but people not affiliated with universities have a much worse time.  Independent researchers must pay thousands of dollars to inform themselves about important scientific issues.  Monbiot refers to it as, “a stifling of the public mind.”  He goes as far to say it goes against the universal declaration of human rights, which states, “everyone has the right freely to … share in scientific advancement and its benefits.”

Open-access publishing has some great resources, but it has failed to take down the publishing monopolies.  In 2010 Elsevier’s profit margin was the same as it was in 1998.  The reason for this is that big publishers own the journals that have the biggest academic impact, which researchers must publish in if they want to secure grants and advance their careers.  As Monbiot puts it, “you can start reading open-access journals, but you can’t stop reading the closed ones.”

Meaningless Waffle

Government have not done much to stop it.  The National Institutes of Health in the US has encouraged those seeking their grants to put their papers in an open-access archive.  The UK government, however, has done nothing.  Monbiot refers to their statements on the matter as, “meaningless waffle.”

He suggests that in the short-term, governments should investigate these publishers for holding monopolies, and they should insist that all papers written from publicly funded research be put into a free public database.  In the longer-term, they should work to create a single global archive of academic literature and data that would be peer-reviewed by an independent body and funded by the parts of library budgets that are currently being spent on journals.

The Plight of Books

Kathleen Fitzpatrick, in her article “Planned Obsolescence: Publishing, Technology, and the Future of the Academy,” begins by relating the tale of a book she had written about how the book was not dead.  The publisher had the book under review for ten months, and then sent her an email saying her book was good, but it posed “too much financial risk… in the current economy.”  This scenario highlights was Fitzpatrick thinks one of the most significant problems facing academic publishing today – an insupportable economic model.

During hard economic times, two university departments whose budgets are hit the hardest are university presses and libraries.  Any cut to libraries means further cuts to the press, because the high cost of academic journals means libraries manage their budget cuts by buying further books, as already discussed.  The effect on libraries is not felt as strongly because they are able to turn to consortial collection sharing, but the effect on presses is devastating.  For example, the Harvard University Press could previously count on every library in the University of California system buying a copy of each book they published.  Since 2001, however, the pattern has been the one library in the system will buy the title for all the libraries to share.  This same pattern has been established at almost every university system in the country, with the result being that by 2004 sales of academic books to libraries had fallen to less than a third of what they used to be.  The average university press receives less than 10% of its budget from its institution, meaning it is very dependent on the sales of it publications to keep it afloat.  Fitzpatrick thinks its because of this model that university press publishing is not sustainable in its current form.

The Living Dead

She describes the industry as being “undead,” because it’s still required in the academic world but it is not viable.  She argues that moving to digital forms of publication is not enough to save the industry.  In fact, she thinks it may cause more problems than ink publication currently has because of constantly improving software that becomes incompatible with older forms of text publication.

Since her experience with academic publishing, she has been working on two projects aimed at creating the changes she thinks are necessary for humanities publishing in the 21st century. The first is MediaCommons, which is a digital scholarly publishing network focused on media studies, that has been developed with the help of the Institute for the Future of the Book, the NYU Digital Library Technology Services group, and an NEH Digital Start-Up Grant.  They have many projects used to facilitate conversations about media, the longest running of which is In Media Res, in which five scholars a week comment on media text as a means of opening discussion.  They also have MediaCommons Press, which publishes longer texts for open discussion.  Some of these texts have moved beyond digital and are now being printed in ink.

Peer Pressure

Her second project, Planned Obsolescence: Publishing, Technology, and the Future of the Academy, focuses not just on technological changes that need to occur for academic publishing to be successful, but also on social, intellectual, and institutional changes that need to occur for academic publishing to be accepted.  Fitzpatrick believes that until scholars believe that publishing on the web is as valuable as publishing in print, few will be willing to risk their careers on it, with the result that digital publishing will remain marginal, undervalued, and risky.

One of the biggest issues in digital publishing is how peer reviews would work.  Fitzpatrick thinks the current system of peer review is broken.  She views it as a back-room dealing between the reviewer and the publisher that excludes the author and impedes the circulation of ideas.  In her estimation, peer review serves mainly a gatekeeping function, as there is a limited amount of space in print and not every article can be published.  However, in the digital world, space is not a problem.  She says that the current system of peer review is not really applicable to digital publication, and says that a new form should occur after publication and focus on how the article has been received.  She says this new peer review system should consist of hits, downloads, comments, and inbound links.  She put the entire text of her own book online for open review a year before it was published, which she deemed an extremely successful experiment.

Fitzpatrick claims that blogs and online publishing networks are in many cases having a greater impact on the scholarly community than traditional peer-reviewed publications.  Although that may be true for the digital humanities, I do not believe that to be the case for any other discipline.

In the end, she says that the issue is not really obsolescence, but rather what our reaction to that obsolescence will be.  Rather than sitting back and complaining about what is happening to the humanities, we should take charge and figure out a way to make the humanities work in the digital age.

Discussion Questions:

1)    Do you think it’s worth it for libraries to buy journal subscriptions, even if the cost means they will have to buy fewer books?

2)    What are some problems you foresee with open publishing? Will the market eventually grow enough to entice major research to be published there?

3)    Should the current system of peer review be used in the digital realm? If not, do you think the system Fitzpatrick advocates should be the one we use instead?

A note on my Prezi:

I chose to start my Prezi with the larger theme, and then zoom in on the smaller issues one by one. I put them in a horizontally linear orientation to represent the progression of ideas and how they all relate to each other in some way. After the discussion of the first article, I zoomed back out to the larger theme before continuing on to the second article to reinforce how each article connected first to the larger theme, and secondly to each other.

That’s Capital: Funding, Resources and Influence

The Link to My Prezi: http://prezi.com/udcc-qyhvxoe/thats-capital-funding-resources-and-influence-in-digital-humanities/

One of the biggest issues facing the Digital Humanities field today is how to gather the resources needed to execute the ideas of the scholars and designers working in the field. This is not limited to financial resources, though those are important. Digital Humanists also need to gather time, people and social capital.

Two of our readings this week- the second chapter of “Our Cultural Commonwealth” and “Innkeepers in a Roach Motel” by Dorothea Salo- deal with these issues. “Our Cultural Commonwealth” does so in a more general way- talking about issues that affect many types of Digital Humanities projects. “Inn Keepers in a Roach Motel” deals more specifically with institutional repositories.

Six Challenges:

“Our Cultural Commonwealth” points out six challenges facing the Digital Humanities. These range from problems with the data itself, to systematic problems within university systems.

  1. Ephemeral Data: basically this is referring to the fact that digital data is easily altered in a way that is untraceable. Also, data can quickly and totally disappear from the internet. An example of this that most of us have probably encountered is on YouTube. Videos can appear and disappear on the site quickly and there are numerous edited copies of the biggest videos- which makes establishing authority difficult. The report also points out that the places that we treat like repositories- YouTube and Facebook (I would also add Twitter to this list) are not designed to act as repositories and therefore are not reliable in the way scholars expect their sources to be.
  2. The nature of Humanities Data: Basically, what they are referring to is that anything might be considered Humanities Data, from the traditional text resources to fashion sketches to graffiti. Digitizing and studying these resources can be difficult, especially since things like dance or other live performance are hard to capture in a digital space, as we’ve discussed already. The report also discusses that sometimes a larger data set is more useful in digital projects than smaller ones, so the Shoah Foundation at USC, for example is more useful than a few individual narratives and large datasets are harder and more costly to build and maintain. They also mention that data can be useful for many types of scholars. For example, the Roman De La Rose project might be useful for historians, art historians and literature scholars and linguists. It’s difficult to predict and provide what all of these disparate groups of scholars might need to conduct research.
  3. Copyright: Basically, this is the same thing that we’ve been talking about all semester. Because of copyright law much of the material produced in this century are not available for scholars to use in their digitization projects, or is costly or difficult to obtain the rights to. Also, this threatens the preservation of born-digital works because by transferring these works from one medium to another, libraries and archives are breaking copyright law.
  4. Conservative Scholarship Culture: There are a lot of facets of these attitudes that are problematic for Digital Humanists, but the largest problem stems from the common perception of the humanities scholar as a lone academic- spending long hours in isolation pouring over books. While researchers in other disciplines have embraced teamwork as a method of conducting research, Humanities scholars have been slow to do so. However, it is exactly this type of cross-discipline teamwork that is necessary for to create Digital Humanities projects.
  5. Culture, Value and Communication: this is probably the longest and most complex part of the problem and it basically stems from problems around scholarly communication. The report describes the traditional relationship between publishers, scholars and libraries, who form the core of scholarly communications, in terms of three economies. The first is a prestige economy, which serves the scholar. The second is the market economy, which is primary for the publisher. The third is the subsidy economy, which is primary for libraries. All of these economies are important for the other two players in the system for which they are not primary, and when they work together they work very well. However, digital modes of publishing threaten this balance, especially when we view scholarly communication as a marketable good. The authors of the report argue that instead we should see scholarly communication as a public good, which should be extended to the public at low or no cost through open access. However, true open access requires new business models for the publishing leg of this triangle. This will be costly and difficult for publishers and libraries to create and maintain (more on this later). However the authors of the report argue that this will end up being less costly in the long run, if universities replace publishers as the providers of scholarly content. They point to things like Project Muse and open access software as evidence that this can work. However, as Dorothea Salo points out, there is a large opposition to a lot of this and many people don’t see open access as an absolute value. These types of changes will also require that everyone come to see scholarly communication as a public good and not a market commodity, which is a large and radical shift that will be difficult for pretty much everyone to make.
  6. Funding: Basically, there just isn’t enough funding. In the US, the government isn’t investing in Digital Humanities research on the same levels as in Europe. There is also not as much money spent on Humanities research in general, especially when compared to the amount of money spent on science and medicine. Private funding is not making up for the deficit in federal funding. The authors of the report propose that digital humanities projects should not just request funding from, but also partner with private companies to create the necessary cyber infrastructure.

“Inn Keeper at a Roach Motel”: Institutional Repositories

Institutional repositories are one of the things that the authors of “Our Cultural Commonwealth” advocate as a solution for the challenges facing Digital Humanities, because they treat scholarly communication as a public good, not a marketable commodity. However, Dorothea Salo, who is a repository manager, argues that digital repositories the way they are typically run now are failing, and doing so at the expense of Open Access as a concept. She likens being a repository manager to being an inn keeper at a roach motel- nobody really wants to go into your establishment and nothing useful comes out of it. She identifies three key players- the faculty, libraries and the software companies that all contribute to the failure of the institutional repository. Oddly, she ignores the role of publishers in this equation, except as a force acting on the faculty.

Faculty: Basically, Salo argues that faculty have rejected institutional repositories as they currently exist because they don’t serve their needs in their current form. Part of it is that they don’t see Open Access as a primary value, and the repository managers haven’t been doing a good job of teaching them why it’s important. Self-Archiving is also difficult for faculty to do themselves and some have privacy and copyright concerns with how the system works now. However, the biggest problems they seem to have is that they see institutional repositories as a threat to their prestige and to the traditional modes of publishing that are favored in the tenure system. Professors seeking tenure are mostly still required to submit a “tenure box” that doesn’t leave a lot of room for born-digital materials. As we’ve discussed before, the younger professors who might be more open to and comfortable with this mode of publishing, also have the most to lose by embracing it under the current tenure system.

Libraries: First, Salo argues, that the librarians themselves are not universal supporters of the institutional repository. They don’t put their own research in there and even though the repository is often run out of the library, they see it as something separate and don’t push the resources it contains as a source for research materials. Part of this is because librarians themselves usually don’t understand what the repository is or why they should support it. This discounts that many librarians are also wary of the institutional repository because there are significant costs associated with running one that take away funding from other areas of the library. As “Our Cultural Commonwealth” points out, many believe that institutional repositories should and will replace traditional publishing methods as a way to obtain scholarly work, but this hasn’t happened yet and there are those in the library community that think that it never will- that participation won’t ever be high enough and the costs will always be too great to supplant traditional methods of acquiring materials. Salo, however, points to another cause. She argues that part of this is due to the four different ways that libraries manage repositories.

  1. 1.       The Maverick Manager: A person brought in from the outside to build or manage the repository. This is usually seen as someone coming in and trying to change things without a clear understanding of how things work at that institution. Because this person is new, they usually don’t have any connections among the faculty to ease this transition, and they have to go through other librarians, who are not always welcoming and even if they are, are already busy with their own duties. Libraries who use this model usually don’t give the manager enough resources to succeed, either in terms of people or budgets to buy and maintain the infrastructure needed, digitize analog content, and educate people about the repository. Basically, this person is usually set up to fail and leaves quickly.
  2. 2.       No Accountability: Under this model, the repository is set up and the responsibility for it is dispersed among the existing librarians, who already have substantial amounts of responsibility. Because the learning curve on the software and the issues surrounding the repository is so high, the repository does not become a priority. Also, usually under this model, faculty are responsible for voluntarily placing their own materials in the repository, which they generally will not do.
  3. 3.       Consortial Management: In this system, libraries join together with other academic libraries and form a repository that is managed by a technical staff employed by the consortium and a committee containing members from all of the member organizations. However, by outsourcing the system, technical support becomes more difficult and faculty outreach is almost non-existent.  Also, the committee members usually don’t have the power and influence at their home institutions to shape policy to promote the repository. Also, because the repository is funded by multiple campuses, if one decides to stall funding, the whole system can collapse.
  4. 4.       Cooperative: In this model, librarians work with the faculty who, in many cases may have initiated the repository in the first place. They mediate deposits, something that is missing from all of the other models and they develop all types of resources to improving and maintaining the repository. This model is expensive, but it has seen the most success.

Software: The software used to manage digital repositories is not conducive to doing what the faculty want the repository to do. It does not allow for versioning or for various levels of privacy, which would make it a useful tool for sharing research. It is also extremely hard to customize and very buggy. It takes a skilled Java programmer to customize the software, and even then technical support for the software is hard to obtain and harder to dole out to the faculty users. Since it assumes that faculty will be uploading the materials themselves, licensing someone else, like a secretary or a library staff member to upload the material is complicated and usually involves some sort of paper process that is onerous for the faculty. Even if the faculty does do their own uploading, the process is not straightforward and any mistakes have to be corrected by completely removing the item in question and re-uploading it. The interfaces provided are also not user friendly and even minor modifications, like putting a new skin on the system to match the school’s existing web design, for example, is difficult and time-consuming and frequently leads to incompatibilities with newer versions of the software. This is made even more difficult by the fact that most librarians are not trained IT managers and they have to rely on a separate IT staff that may not be responsive to the requests of the librarian.

Moving Forward:

Salo gives 11 suggestions for what should happen with institutional repositories going forwards. I will quickly go over them here.

  1. Home Support- The repository should work to gain support from within the library and mandate that all research produced by librarians go into the repsository
  2. Launch with a constituency- new repositories should not be formed without at least one significant constituency that is actively seeking a repository. This might be a faculty member or a desire to archive student theses and dissertations.
  3. Look beyond peer reviewed literature- seek to include scholarly grey material and early drafts of work.
  4. Collect actively- instead of waiting for materials to be put into the repository, repository managers should actively seek and collect materials from the faculty
  5. Seek forgiveness rather than permission- faculty who wish their work to be included in the repository should sign a blanket license, giving the repository manager permission to include all of their research in the repository instead of having to give permission for each individual work
  6. Digitize analog content- exactly what it sounds like, digitize faculty’s older works and their newer works that appear in print form, since so many do not simply produce born-digital material.
  7. Work with discipline repositories- get licenses to harvest the works put into disciplinary repositories and give those repositories license to harvest revelant materials from the institutional repository.
  8. Involvement with Cyber Infrastructure and Faculty Data Management- Repository managers should not leave the entire infrastructure up to the IT professionals, and should be more directly involved in how the systems are set up and run. They should also provide support for all of the ways that faculty manage their scholarly data.
  9. Contribute to software development- the designers of this software should seek out feedback from those who run the repositories and repository managers should involve themselves more directly in the software development process.
  10. Faculty prestige- the repository should keep statistics on what materials are viewed and how often, in order to give the faculty a metric for measuring their prestige, which is a large part of tenure considerations.
  11. Integrate with other Campus IT systems- the repository should be compatible with classroom software like Blackboard and should also be easily integrated into the faculty’s personal websites. Not only to advertise the repository, but so the repository can pull materials from these sources.

Conclusions:

Overall, both of these articles give a good view of some of the challenges facing the Digital Humanities and institutional repositories. They also provide good solutions to some of the problems. Both argue that the people running these projects need more capital- whether that is in the form of actual money or social capital.

Discussion Questions:

Did the authors of “Our Cultural Commonwealth” miss anything? Has anything changed in the 5 years since this report was written?

Do you agree with Salo about the flaws in institutional repositories? Can you think of any other ways to fix the system? Is it worth fixing?

A Note About my Prezi:

I chose to design my Prezi in a series of closely spaced shapes to show how closely related these articles were. I chose a circle wheel design for the section dealing with “Our Cultural Commonwealth” because each of the challenges discussed seemed equally important. I chose to design the section about “Inn Keeper in a Roach Motel” as two hotels, connected by a road because I felt that the Salo article laid out the current “inhabitants” of the motel and then gave a set of guide posts (represented here by the road signs) for moving forward into the nice, clean hotel.

Tinker, Tailor, Builder, Maker: Digital Humanists in the World of Big Data

Link to my prezi: http://prezi.com/chuttfitawvw/tinker-tailor-builder-maker-digital-humanists-and-the-world-of-big-data/

Who’s In and Who’s Out of …What Exactly?

There are a lot of questions that tend to make an appearance (like whack-a-moles) in discussions related to Digital Humanities. One of the more contentious questions is who are digital humanists? What are there qualifications?  How do people go about getting “into” the digital humanities field?

Any discussion of who is “in” and who is “out” in digital humanities raises a whole host of other questions and concerns, including issues of skill-sets, qualifications, and, interestingly enough, what the digital humanities are in the first place. Digital humanities, as a burgeoning field, a concept, a profession, a term, is very much in flux.  This ongoing debate makes for some interesting and exciting discussion, which is reflected in the readings I am presenting.

Big Data: Go Big or Go Home

To set the stage, I’d like to start with our friend Lev Manovich and his article Trending: The Promises and the Challenges of Big Social Data. In this piece, Manovich discusses the role big data is playing in the digital humanities, and the impact big data is having on those who engage in the digital humanities in their work.

What is “big data”?  According to Manovich, big data is, in a nutshell, massive amounts of data.  In 2008 Wired magazine noted that we’re entering a new “Petabyte” age of big data, where our ability to handle massive data sets is growing and changing.

The NEH Office of Digital Humanities has recognized this shift and has issued a Digging into Data Challenge. The NEH notes that “As the world becomes increasingly digital, new techniques will be needed to search, analyze, and understand these everyday materials.”

Manovich considers many projects that tackle big data in various ways and forms. Manovich’s own Software Studies Initiative is a forum where software is considered as a subject of academic inquiry. Tools like Many Eyes and Tableau offer free data analysis and visualization. The List of Data Repositories at the Digging into Data project shows just how many organizations are making large amounts of data available for study.

Digital humanists now have the opportunity to handle and explore large data sets. But this opportunity is also something of a burden. As digital humanists start working with things like billions of tweets or Flickr photos, a number of theoretical and practical issues arise.

Surface Data vs. Deep Data

Manovich identifies a few major issues that surround the use of big data in the digital humanities. The first issue is the differences between “deep data” about a few people and “surface data” about many people. Traditionally, deep data has been the realm of the humanities, while surface data has been the domain of social scientists using quantitative methods.  Existing between these two data arenas was the land of statistics and sampling.

According to Manovich, big data is changing the data landscape. Can we really distinguish between deep and surface data now? Manovich agrees that the type and amount of data we can now access is changing the research landscape. But he also raises a few objections to the idea that big data has wiped out any distinctions between deep data and surface data.

His first objection is that only social media companies have access to large data sets. The public APIs that researchers can access do not contain all the user data that a company has. The data a researcher gets might be massive, but it does lack depth.

Second, Manovich discusses issues surrounding the authenticity of digital footprints, and considers cases of self-censoring and governmental censorship online.

Aside from these more practical objections, Manovich also notes that different types of researchers access different kinds of data and, therefore, ask different questions in their research. Digital humanists will inevitably have a different approach to data, big, deep, or surface, than a computer scientist researcher.

So the question for digital humanists becomes one of relating technique to theory. Manovich asks “What can be discovered and understood with computer analysis of social and cultural data?” Computer can gather information, but they are not very good at analyzing data. Just look at Watson’s performance on Jeopardy, where he (it?) had trouble with more nuanced phrases and reasoning.

For digital humanists, the ideal may be to have computers parse large amounts of data and humans interpret the data.

Dealing with Big Data

But are digital humanists up to the task of dealing with big data?  Manovich concedes that some technical know-how is needed to really do a lot with tools.  According to him there are three types of people: those who create data (everyone), those who can collect data, and those who can analyze it. The gap for many digital humanists lies in having the means to collect data. Whether it’s an issue of funding, a lack of public tools, or a lack of knowledge in using tools, many digital humanists face obstacles in actually accessing and parsing big data.

Surface data might be on its way to becoming the new deep data for digital humanists, but there are many practical obstacles to overcome before that theory is a reality.

Computer Education 101

Various types of data, and the necessity of using tools to deal with big data, are now part of the digital humanities landscape. But, as Manovich noted, there is a knowledge gap among many digital humanists in terms of computer savvy.  Do digital humanists need remedial digital education?

Into the breach step James J. Lu, computer science and mathematics professor at Emory, and George H.L. Fletcher, engineering and computer science professor at Washington State. In a nutshell, these two men argue that we need to teach computer science and computational thinking early and often.  Sounds good, right?

Yes and no. This article is really a wonderful example of why humanists are really quite important in the world. The problem with this article is that the engineer and computer scientist authors do not seem to know much of anything about primary school education (and they appeared to use only the Internet as a resource for grade school curriculum ideas).

At any rate, they argue that we should focus on teaching kids “computational thinking” rather than programming. In fact, no one should learn programming until they are older and in high school or college. Children should learn the theory and concepts behind computers before beginning to program.

Would this type of education be useful at all?  A potential counter argument comes from UT’s own Computer Science Department. Their outreach activities to elementary schools are all quite hands-on and practical and teach basic programming concepts as opposed to straight theory.  Likewise, a course in Computer Science for Non-Majors features a mix of practical concepts, programming basics, and information on how computers actually work.

The real lesson from this article seems to be that humanists (such as educators) should work with computer scientists to improve school curriculum and turn out a generation of tech-savvy humanists who can parse big data with the best of them.

Digital Humanities Throw-down, 2011

Educating children about computers (a noble aspiration) is an area of interest, but what of the current, adult digital humanists who likely never learned programming in grade school?  Is programming even necessary for digital humanists to learn?

According to Stephen Ramsay, the answer is yes.  At the 2011 MLA conference, Ramsay set off something of a firestorm when he insisted that digital humanists need to know how to code. Ramsay said that “If you are not making anything, you are not … a digital humanist.”

Ramsay took to his own blog in the aftermath, to clarify his position.  While he expanded on it, he never really took back his initial assertion.  To Ramsay, making is an essential and intrinsic part of the digital humanities. Making, and the process of doing so, “yields insights that are difficult to acquire otherwise.”

In clarifying his stance, Ramsay amends that coding per-se is not necessary for digital humanists to know. But, if you plan to put digital humanist on your business card, you have to be involved in building something.  Ramsay argues that all aspects of the digital humanities (data mining, XML encoding, GIS, tool design, etc.) involve building.

Bricoleurs and Collaborators

Alan Liu commented on Ramsay’s post with a thoughtful counter-suggestion (argument is a bit strong for what Liu did).

Liu notes that many digital humanists are bricoleurs of code; they borrow and patch things together. In fact, digital humanists have a lot in common with engineers. Liu says that structural engineers do calculations and make drawings, but a whole host of other roles are needed in order to actually build a structure.

The idea of having makers/builders be “in” the digital humanities clubhouse excludes the idea of collaboration, which is at the heart of digital humanities.

Liu concludes by saying that we should recognize a multiplicity of building roles in the digital humanities. In this respect it seems that the digital humanities should be no different from the more traditionally defined humanities in that there are many different types of humanists doing many different types of work.

Defining the Digital Humanities

Mark Sample’s post “The digital humanities is not about building, it’s about sharing,” also responds to Ramsay’s rather contentious remarks with a thoughtful assessment of what the digital humanities are and who digital humanists are.

In the digital humanities field, there’s a definite tension between “do vs. think.” But Sample argues that these tensions and debates are little more than a “distracting sideshow to the true power of the digital humanities, which has nothing to do with production of either tools or research. The heart of the digital humanities is not the production of knowledge; it’s the reproduction of knowledge.”

Appropriately enough for a digital humanist, Sample thinks that he really nailed his belief down in 140 character or less on Twitter:  “DH shouldn’t only be about the production of knowledge. It’s about challenging the ways that knowledge is represented and shared.”

Now that we are back into the humanistic happy place, where sharing is caring, Sample turns his piece to a discussion of how digital humanities can pioneer ways to share knowledge. Sample is particularly energized by the new head of the MLA’s Office of Scholarly Communication, Kathleen Fitzpatrick, who is a founder of Media Commons.  He hopes that Fitzpatrick will be an advocate for positive change in the academy. Some of Sample’s ideas for the future include scholars starting their own small academic press, the abolition of blind-review, and the accepting digital projects as tenure-worthy.

In closing, Sample considers the digital humanists, the makers and creators and builders and bricoleurs, as more than just those who create or use tools. To Sample, a digital humanist is someone who has “the opportunity to distribute knowledge more fairly, and in greater forms.”

Ultimately, digital humanists are not just making tools and techniques and theories; they are “making” a landscape where various kinds of knowledge are widely available.

Discussion Questions

At the very end of his article, Manovich raises an issue about privacy. He writes “would you trust academic researchers to have all your communication and behavior data automatically captured?”  Well, would you?

Do you agree with Ramsay’s assertions that digital humanists need to know how to code, or at the very least be “building” something?

Do you agree with Sample about the overarching goal of the digital humanities, to reproduce and share knowledge?  What are some other ways digital humanists can accomplish this?

A Note on my Prezi Presentation

Is prezi presentation redundant?  At any rate, I designed my presentation (using the word design rather loosely) to loosely resemble a web of circles and lines. Unoriginal, yes, but I felt that a web framework best expressed the interlocking questions and issues that I explored in my presentation. Considerations of who digital humanists are, what they do, and what digital humanities is in the first place are all linked together. I also hoped that my design would help to demonstrate the slightly messy and very complicated nature of these questions and issues in the digital humanities. Additionally, I also chose to spread my presentation out in terms of space, to represent the wide-ranging nature of the questions posed throughout the presentation. In terms of colors, I opted to use splashes of primary colors (in the CMYK model, salient to both paper printing and computers) to represent the essential nature of these questions and issues to the field as a whole.

Capta and Data: Visualization, the Humanistic Method, and Representing Knowledge

While visualization has become an increasingly useful and important tool for humanists wishing to illustrate large aggregations of data that can be shown in such a way that it can be easily viewed and comprehended at a glance, visualization tools pose many challenges and are not without their caveats.  This has prompted many scholars to propose new ways of understanding visualization as a humanities-centered tool that on the one hand challenges traditional conceptions of the relationship between visualization and the data being represented in all disciplines, not just those that are humanistic, thus redefining what visualization is and is capable of doing, and on the other hand attempting to accurately represent humanistic data without compromising its situatedness and socially constructed nature.

Before moving forward, however, it is important to understand what visualization is and why it is increasingly used as a tool in the humanities.  Visualization is essentially the process of representing aggregate data in such a way that it can be digested and understood with relative ease.  As Manovich notes in his article, the process of visualization involves translating the world into numbers and then visualizing the relations between these numbers. Increasingly, humanists are utilizing visualization, which is manifested in graphical tools, in order to explore and render data and information in new ways.  Let’s say we wanted a way of being able to view the flow and frequency of tweets in twitter feeds within a short period of time, how might we go about rendering this information in a comprehensive and efficient way?  Without visualization tools, this feat would be rather difficult.  You could measure the number of followers notified over time of various tweets, which could be illustrated using a traditional Cartesian graph, for instance, but this wouldn’t take into account that not all tweets are the same, thus eliminating the inherent individuality of tweets.  Therefore, representing tweets with visualization presents the information in a more nuanced and situated way.

In her article “Humanities Approaches to Graphical Display,” Johanna Drucker discusses the importance of representing data produced by humanists as nuanced, constructed, and situated, while also recognizing the performative nature of interpretation.  Since visualization tools are traditionally used by the natural and social sciences, they carry with them the somewhat erroneous assumption that such data being represented is produced empirically and independently of the observer – i.e. that the data is an accurate representation of reality.  This assumes that the observation of a certain phenomena is the same as the phenomena itself.  Drucker thus makes a distinction between “capta” and “data,” arguing that the former is a much more accurate word to describe the production and representation of knowledge in the humanities, and elsewhere.  The word “data” comes from Latin to mean “given,” whereas the word “capta” is derived from the Latin word for “taken.”  Thus, she writes, “Capta is “taken” actively while data is assumed to be a “given” able to be recorded and observed.”  The idea of “capta” then better represents the humanistic method of knowledge production as situated, partial, and constituitive.  She notes that this does not mean that the use of “data” in the sciences and “capta” in the humanities juxtaposes the two disciplines in an intellectual opposition, but rather that humanists are perhaps more aware of the fact that “intellectual disciplines create the objects of their inquiry.”

How then, do we represent capta and account for the interpretative nature of knowledge, or display it in a qualitative manner?  If you wanted to study represent the percentages of immigrant men and women in various nations at a given time, this information might be displayed in a bar chart.  Such information, however, is portrayed as deceptively simple and fixed.  A bar chart does not account for what constitutes as a permeable “nation,” the transient nature of immigrant populations, while also assuming a simplistic, binary distinction between genders.  Another visualization is needed in order to display gender ambiguity, different naturalization rules, shifting borders of nations, among other conditional expressions of interpretation.

One such notable project that synthesizes large sets of data, or capta, using visualization tools is the Republic of Letters, a database of a large corpus of correspondence exchanged between prominent intellectuals in the 17th and 18th centuries.  Comprising over 55,000 letters and hosted by Stanford University, the Republic of Letters combines geographical imaging and computer technology to map and illustrate the complexity of the international network and flow of information between these intellectuals through correspondence.  The developers used a metadata table from their database called the Electronic Enlightenment that accounts for spatial, temporal, and nominal attributes.  While it proved nearly impossible to render the entire dataset of every intellectual’s correspondence in one visualization, the project designers created an interface that would allow the user to select multiple views of graphs and animations to depict different attributes.  For instance, if you wanted to see the network of connections of Voltaire’s correspondence from 1700 through 1750, this tool allows you to view the top cities his correspondence both came from and was delivered to, as well as his top correspondents.  Likewise, you can also see the volume of correspondence to and from each city.  The Republic of Letters allows scholars to make sense of large set of historical data (or capta) and see patterns that would otherwise be difficult to explore.

While illustrating large sets of historical data poses its own challenges, how to represent visual collections presents different challenges, the largest challenge being how to display an entire collection at a glance.  In his article, “Media Visualization: Visual Techniques for Exploring Large Media Collections,” Lev Manovich notes that having rich metadata would at first seem like a possible solution to this problem, yet even sites like GettyImages, which has substantial metadata for its media collections, cannot efficiently illustrate large patterns.  In addition, most online media collections, such those of the Library of Congress, have interfaces which display the collections in hierarchical categories, which is a 19th century technology, while also yielding the images through basic mid-20th century information retrieval methods.  Manovich saw a need to create a technique that would allow researchers to see a comprehensive view of such collections at a glance.  The solution was to create media “landscapes” that convey enough details while being compatible with human information processing rates.  As I noted before, the typical process of visualization involved translating the world, or the capta/data into numbers and then finding a way to visualize the relationship between those numbers.  Manovich and developers from the Software Studies Initiative, however, realized that with media visualization, in order to be able to view a collection of media images in a comprehensive way, a set of images would need to be translated into a new image, or taking pictures (plural) and translating them into one picture.  Thus, he defines media visualization as “creating new visual representations from the visual objects in a collection.”  This method contrasts with content analysis in that it does not require the time-consuming creation of new metadata for images.

According to Manovich, this media visualization is analogous to the idea “mapping,” or rather “remapping,” in order to create new maps of media universes and landscapes.  This method challenges our conventional understanding of cultural image sets as being defined by metadata.  For example, they took the covers of Time Magazine and created a visualization that depicts the change in the color saturation and/or brightness over time.  Another way they represented the covers was to create a montage that illustrates gradual temporal changes, or a method he calls “Zoom Out.”  A method such as “Zoom Out” yields a number of patterns, such as the changing medium of covers from photography, to painting, and then back to photography again; or the shift in black and white covers to color; or the change in content from portraits of individuals depicted in front of neutral backgrounds, to backgrounds featuring compositions of various concepts.  In other words, it reveals a “meta-pattern.”  Besides “Zoom Out,” other techniques include temporal and spatial sampling, which entails selecting a representative subset of images from a larger image sequence, such as another visualization of Time covers composed of slices of the cover images in order to reveal patterns.  These techniques can be emulated by anyone, such as by using Photoshop or an open source media utilities tool like ImageMagic, and customized macros are available at www.softwarestudies.com.

Beyond recognizing the situated and interpretative nature of information, and creating these various methods for representing large sets of historical or visual data, how does visualization change the way we think about representation?  More importantly, how we can reconcile our inability to ever truly create authentic representations of reality with the visualizations we do create in an attempt to render such realties comprehensible in one place?  In her article “From Data Realism to Dada Aggregations: Visualizations in Digital Art, Humanities, and Popular Culture,” Denisa Kera discusses what she calls “dada,” or anti-realism, visualization processes that use the techniques of visualization not to aggregate data, but rather to show the futility of trying to do so.  For instance, artistic visualizations and popular mashups attempt to illustrate the limitations of portraying a static reality or statistical patterns visually, while also serving as tools for social and political criticism.  A mashup such as Swearing on Twitter, which combines Google maps and twitter chats, allows you to see in real time how people use swear words around the world.  Kera writes that mashups and web 2.0 tools such as these “are the compasses and the maps exploring our present condition that is constantly reshaping and redefining new networks and borders connected to new datasets and databases.”

In thinking then about the possibilities, as well as the limitations of visualization, I’d like to end with a excerpt from Jorge Luis Borges’ On Exactitude in Science, a cautionary narrative on creating maps, which warns us against believing that we can ever have a truly complete and authentic visualization:

“In that Empire, the Art of Cartography attained such Perfection that the map of a single

Province occupied the entirety of a City, and the map of the Empire, the entirety of a

Province. In time, those Unconscionable Maps no longer satisfied, and the Cartographers

Guilds struck a Map of the Empire whose size was that of the Empire, and which coin-

cided point for point with it. The following Generations, who were not so fond of the

Study of Cartography as their Forebears had been, saw that that vast Map was Useless,

and not without some Pitilessness was it, that they delivered it up to the Inclemencies of

Sun and Winters. In the Deserts of the West, still today, there are Tattered Ruins of that

Map, inhabited by Animals and Beggars; in all the Land there is no other Relic of the

Disciplines of Geography.”

Discussion questions:

  1. Do you think the tools presented by the Republic of Letters and Manovich’s Media Visualization article efficiently represent large sets of data?
  2. Are there other visualization projects that you think do this well?
  3. Do you agree with Kera that visualization is really an illustration of our futility to be able to accurately aggregate data?
  4. Do you think the distinction between “capta” and “data” is useful?
  5.  What does the seemingly blurry distinction between art and visualization portend for its use as a tool for scholarship?

Link to prezi: http://prezi.com/hcb07xtcmntl/capta-and-data-visualization-the-humanistic-method-and-representing-knowledge/

From a Distance: The Possibilities and Perils of Big Data

Using computers to analyze really large sets of humanities data opens up new avenues for learning that just aren’t possible for a human alone. As Jean-Baptiste Michel and Erez Lieberman Aiden explain in this talk about their Culturomics project, this kind of work can allow us to achieve a maximum rating on the practical vs. awesome scale. That is, reading and analyzing 5 million books would be extremely awesome, but not so practical; reading just a few books very very carefully is extremely practical, but not so awesome. Using a tool such as the n-gram viewer that grew out of the Culturomics project allows anyone to analyze certain aspects of a 5-million book corpus – which is both extremely practical AND extremely awesome. By looking at data from a distance, you can discover a lot of equally awesome things. But it’s important to remember that computing can only take you so far.

“Distant reading” of texts is an idea that lends itself to digital humanists as we explore the research possibilities of extremely large data sets, such as the Google Books project. Two of our readings this week discuss this concept (Sculley and Pasanek,2008; Clement, 2008). Coined by Franco Moretti (2000), distant reading “aims to understand the larger structures within which [a novel … comes to] have meaning in the first place” (Moretti as quoted in Sculley and Pasanek, 2008).

But how well does the idea of distant reading lend itself to machine learning? Sculley and Pasanek discuss four assumptions that the theory of computational learning makes when considering data mining:

  1. Examples are drawn from a fixed distribution. This means that the process which produces the data – for example, an author composing a text – will produce consistent results which can be predicted.
  2. The hypothesis space C is restricted. This means that the experimenter must somehow decide what form of hypothesis will work best for the experiment – for example, in their experiment, Sculley and Pasanek specifically explored the use of metaphor.
  3. The data is well represented. This means that whatever simplified representation is being used in an experiment cannot be oversimplified, as this could lead to misinterpretation of the data.
  4. There is no free lunch. Just because we’ve been able to pull out an interesting set of data results doesn’t mean we can take them on their face as proof of anything – we need to examine both literary and computational assumptions before interpreting the results.

Obviously, some of these assumptions don’t lend themselves well to humanities research. I thought that number one was especially problematic when considering literature – one of the things that makes an good author really interesting is that she may not produce consistent, predictable results. But even within that challenge, you can still use a distant reading of a text to make new discoveries.

So, how do these ideas work out when we actually apply them to a study? Sculley and Pasanek analyzed the use of metaphor in political writings to test George Lakoff’s theory that this could predict party affiliation. They conducted several different experiments using the Mind is a Metaphor database. They go into quite a lot of detail about the various ways in which they interpret different sets of results, but in short, some of these experiments supported Lakoff’s theory, and some of them did not. None of the results could be interpreted as “proof” in isolation – even in “distant reading” we need to apply our own interpretation to results. Still, they do provide an insight into this idea – insight which could not be gained without the use of big data.

Clement also used distant reading to examine Gertrude Stein’s The Making of Americans. In large part due to Stein’s heavy use of repetition, the novel is virtually unreadable in a traditional linear fashion. However, using the tools of the MONK Project, she was able to discover a conscious and deliberate structure to the repetitiveness of the text. This distant reading allows us to make a closer reading with a better understanding of the author’s intent – as Clement describes it, a kind of “map key.”

Michel et al. (2011) had the largest data set of all the studies we looked at this week – five million books, which entails a very distant reading. The best way to experience the results of their study is to check out the Google Ngram viewer for yourself. You can track the use of irregular verbs over time – try querying thrived and throve. You can track the popularity of concepts (global warming, slavery). Or you can check for evidence of suppression or propaganda by looking for an unexpected dip or spike – try looking up Marc Chagall in English and in German, and you’ll see that whereas in English, the artist’s prominence steadily rises, in German, it rises, briefly drops to nil, and then sharply jumps up again.

While you can make a lot of very interesting discoveries using this data, it’s important to note that there are limitations. OCR is not perfect, especially in older texts – for example, the more ornate “s” may be misinterpreted as an “f,” throwing off a query. And as always, the results of any query are always open to interpretation.

So now that we’ve examined some of the things we can do with big data, how can we continue to develop data that can be incorporated into big data sets? Unsworth (2011) discusses the importance of interoperability – both in terms of the text itself, and the rights to the text. For TEI to be interoperable, it would need to afford the ability to take one system’s output and run it, “as is,” on a different system, rather than sending it through any form of translation. Currently, TEI does not support this level of interoperability, but Unsworth thinks that this aspect should be more developed – though the process is not without challenges.

“Once you start to aggregate these resources and combine them in a new context and for a new purpose, you find out, in practical terms, what it means to say that their creators really only envisioned them being processed in their original context,” he quotes himself from a 2007 speech. Even if creators know how important standards and interoperability are, the wide variety of needs between different collections may mean the standards need to be customized anyway. Unsworth thinks that the easiest way to deal with the challenge is to make interoperability a step in the process, and not necessarily the final step, which will allow for different levels of customization as needed in different stages of analysis.

Interoperability also applies to rights information. Michel points out in the TED talk above, five million books could equal five million plaintiffs in a massive lawsuit. The solution that was worked out with Google Ngram was to make the actual texts only partially available – you can only see small snippets of the page where the n-gram occurs. Unsworth points out that this idea of “non-consumptive” research will not be a viable solution for research projects where people need to actually see and read larger portions of content. Still, only working with public domain material can still offer a lot of possibilities – it’s another compromise on the practical vs. awesome scale.

Ultimately, Unsworth says, to be relevant and sustainable, TEI needs to think about promoting interoperability for those creating big data sets, or parts of big data sets.

One of the main ideas that came up in several of the readings was the idea of circularity – in order to truly understand a text, we must go from close reading to distant reading and back again. Sculley and Pasanek describe this idea most explicitly, but its implications are evident in the other readings as well. Looking at the text as a whole, situated in a larger context, helps add insight to all the smaller parts which make it up. But the reverse is true as well – understanding all the parts a text is made up of will help us understand the text as a whole. On both levels, we still need to remember that we are projecting meaning into our interpretations, either of the text itself or the results of a distant reading.

In conclusion, Sculley and Pasanek make several recommendations:

  1. Make assumptions explicit.
  2. Use multiple representations and methodologies.
  3. Report all trials.
  4. Make data available and methods reproducible.
  5. Engage in peer review of methodology.

By employing these recommendations, scholars can better “highlight the varying facets of complex issues.” It doesn’t mean that any one study will result in “proof” of a hypothesis. But we can learn a variety of perspectives on how we can interpret meaning within a text.

Link to Prezi: http://prezi.com/w6vbep7o7ysc/big-data/

Documenting Dance: A Practical Guide

 

Documenting Dance serves as a beginner’s guide to documenting dance performance as well as dance history. The guide is comprised of a very comprehensive breakdown of how to best authentically document performance, what materials are considered supplemental documentation or archival records, what reasons there are to document dance, and eight unique examples of successful dance documentation projects. Interestingly, the guide not only serves as a reference work, but also argues for the urgency in needing to document dance to preserve dance history and culture. In today’s economic climate, Documenting Dance raises awareness for dance history and almost acts as a call for documentation to improve its record-keeping. Thus, it includes not only the tools to document but also a myriad of reasons why. I am going to go through this guide with a very archival view because that is my own background and focus, but I believe all information professionals can contribute their own perspective. For the purposes of this presentation and time limits, I will discuss all of the tools but only discuss selected frameworks and examples that best fit the context of Digital Humanities.

One important theme throughout the guide, particularly in the introduction, was raising awareness through conscious planning for documentation and preservation. I liked the fact that the guide is a collaborative work and can assist a variety of dance practitioners, scholars, filmmakers, and information professionals. The guide describes the long-term benefits of documentation as providing study and analysis of dance while the short-term goals are publicity and funding. I think the most interesting point that the introduction makes is that not only will documentation provide more materials for dance scholars to study, but also that having accurate and detailed records will perhaps encourage more scholarship in the first place, catalyzing more recognition, publicity, and most likely funding. On page seven the guide states, “With good record-keeping of the role of dance in art and in culture, scholars can better develop both the theory and the criticism that will ensure dance’s place in academe…There are three components of dance documentation: representing the process, representing the performance event, and representing the cultural impact.” However, these three components of documentation are easier said than done and this brings up the issues of the topic for the class, which are authority and authenticity in documentation.

Tools for Documentation

Right away, the section begins with, “In themselves, even the tools do not ensure a usable or attractive documentation of a dance. In any project, the aesthetic and informational value depends on skill, which might be defined as the artfulness of the application or tool.” So, just using the tools is not truly enough to create an authentic record.

The first tool certainly makes this statement clear. Dance notation creates a written record of the dance’s form and style and has been used for centuries in the same way music notation is used to document and provide a guide for music. Dance notation is also based on codified symbols that can be used to recreate or study a dance. There are also different systems. Labanotation, provides symbols for the quality of movement as well as the specific steps and patterns. Labanotation was created by dance theorist Rudolf Laban. He originally studied sculpture and was coming from an art background. Laban became interested in the relationship between the moving human form and the space which surrounds it. He established the Choreographic Institute in Munich in 1915 and in 1928 published the dance notation system, Labanotation. There are free scores of his work as part of the International Music Library Project online. Benesh Movement Notation was created by Rudolf Benesh in 1955. Benesh was actually a mathematician in England. His wife Joan was a dancer and he created a notation system to help her decipher and remember her dance steps. Benesh and Labanotation systems are the most widely used dance notations. Dance notation requires patience and a great deal of study before it can be implemented and dances that have been notated are regarded as the most authoritative over other versions or variants. Computer programs have made dance notation faster and utilizes a computer’s ability to analyze patterns, shapes, and quality of movements. The Laban Writer program is open source software anyone can download and use. The site has guides and examples to learn the codified system. If a dance is notated it becomes a part of the Dance Notation Bureau. They maintain an archive of notated scores and also provide guides to notation systems.

The guide recommends hiring or becoming a certified expert and submitting notations as well as all other supplemental materials to the Dance Notation Bureau. Notation is a long-term record of the original style of the dance and has a high likelihood of accuracy because it can include descriptions, positions, and performance qualities of all the dancers included in the production. Notation also allows dances to be reconstructed. For example, the Feuillet notation system, which was used for baroque-era dances and included comprehensive descriptions is a testimony to the importance of notation systems in keeping dance history alive. The detailed instructions on how to decode the Feuillet scores motivated researchers to learn the system and decode it. The Early Dance project includes Feuillet notated choreographies.

Of course, the most authentic and authoritative documentation system is the most difficult to accomplish. Notation takes long periods to do well and only so many people are certified. Furthermore, only so many people can actually read and understand notation once it is done. One idea I would like to point out is that through my research of dance notation I found quite a few digital humanities projects, beyond the ones I showed today, that work to educate on dance history.

Film and video appears to be the most obvious and easiest tool to document dance. Videotaping a dance performance is increasingly affordable and videographers can learn how to best film a dance through working with the dancers and choreographer. For this method, planning is especially important. Videographers can implement storyboarding to ensure that specific movements are captured and dances can be reshot multiple times. Footage can be indexed to highlight important parts of a performance. The video can also be shot at a greater number of frames per second to show detailed footwork. Today, digital images are very high quality and DVD formats are widely available.

The disadvantages of videotaping all contribute to the fact that it is the least authentic method for documentation. A video may not be able to achieve the correct lighting, details are left out when filmed from afar, and percussive forms of dance require that both sound and image are synchronous. The guide names the 1989 film, Tap, starring Gregory Hines, as a successful film recording of dance. Unfortunately, carriers of film are all subject to deterioration and formats are now rapidly becoming obsolete with the quick advancement of new technologies. Videographers also have to be mindful of equipment failures. Lastly, copyright can be a huge problem for widespread accessibility of the film because of dance and choreographer unions.

Motion capture is essentially the combination of dance notation and video recording. Sensors are placed to specific parts of the dancer’s body and information about their movement is transferred to a computer and reconstructs a figure on the screen. Motion sensors provide the detail that is not available or as precise when videotaping. Dancers are also more three-dimensional and choreography can be analyzed for comparisons. However, if a dance requires falling or floor work, the devices might break. Additionally, the guide asserts that not many dance companies have access to motion capture technology. This guide was published in 2006 and probably written over a number of years. Since then, motion capture technology has improved. I was not able to find a good example of a motion captured dance performance, but I did find a promotional video for the XBOX 360 input device controller Kinect, which was released in 2010. Kinect is based around a web-cam style add-on peripheral that is a motion sensor and allows the user to interact with the game without ever touching a controller. The range camera interprets a 3D scene using Light Coding. Kinect also competes with Wii Remote Plus and Play Station Move. Thus, not only are motion sensors more advanced, but you can purchase one for your own home.

Categories of Evidence

Because capturing a performance is lacking in authenticity and exact replication, the guide gives a detailed list of supplemental “evidence” or information to promote dance history and archival materials. The guide includes recorded evidence which features: notation, dance manuals, reviews, publicity materials, memoirs, correspondence, programs, business records, transcriptions, and music scores. Visual records include: moving images, dances made for new media, documentaries, motion capture, photos, and artworks. Aural evidence includes: sound scores, audiotapes, and interviews. Ancillary evidence includes: production materials such as set designs and costumes/costume designs. The guide also discusses unrecorded evidence and ways to make it recorded evidence such as: creating oral histories, body memory, and transmissions from dance choreographer to dance. All of these materials will depend on the archive or dance company. I thought the inclusion of this list was interesting and perhaps dance companies have a history of not saving their materials because traditionally archives were used for historical records as opposed to arts records.

Frameworks

Frameworks are contexts where dance documentations take place. The guide includes these contexts to show how often dance is studied and analyzed for a variety of purposes. In my opinion, many of these frameworks overlap and can be combined.

The creative framework: To make a dance and art. Through the documentation of the creative process, scholars will be able to study and understand a dance’s context. This is done more in the art world rather than the archives or scholarly world, but would be an interesting process to examine and hold record of.

The transmission framework: The cultural expression of a community group for preservation of and transmission of cultural values. The transmission framework is to tangibly record a dance normally passed down through oral or kinesthetic tradition. Recording these dances would provide more evidence of a rich dance history in specific cultures, particularly non-Western ones, or to better document the community itself.

The archival framework: To collect, inventory, provide access, and preserve materials related to dance. Archives, libraries, and museums accession dance collections. They then catalogue the materials, perform preservation, and write finding aids for public access. This is the most traditional framework to encourage scholarship.

Best Practices: Examples

The guide includes many unique examples of excellent dance documentation and the contexts of each project. These examples provide ideas as well as successes for dance documentation.            

Core of Culture Dance Preservation: Dance in Bhutan—Buddhist Sacred Dances

The Honolulu Academy of the Arts commissioned this project to see transmissions of dances in Buddhist monasteries. Dances had become corrupted because of interactions with tourists at monasteries, so the project sought to document the sacred dances respectfully for education and preservation without public presence. The project leader, Joseph Houseal worked with a specialized staff. He kept a daily log of events as well as descriptions of each dance, his own interpretations, and his conclusions about what needed to further be analyzed. They also used three cameras to record with synchronized time zones. Information about the dance as well as film extracts will go into a database online.

Ohio State University’s Documentation of Anna Sokolow’s Steps of Silence

This project extends the records of an earlier documentation project and utilizes the three tools discussed. Labanotation, video/film, and motion capture were used to supplement each other. The original 1968 production of Anna Sokolow’s Steps of Silence, was commissioned for the Repertory Dance Theatre in Salt Lake City, Utah. It is documented through the footage of a lecture by Sokolow, photographs of rehearsal, a 1970 film about the original cast, interviews with three dancers, and assorted written records. The 1975 revival is recorded in Labanotation by Ray Cook. In 2004, Valerie Mockabee, a notator, compared the film footage from 1968 to the notation score in 1975 and used both to restage the work. Motion capture was used to document moments in the dance where partnering or film angles obscured the movements. The markers were then made into animated 3D figures to see details from various perspectives. The documentation was digitized and made into a DVD set for educational use. The DVD includes, digital footage of rehearsals, footage from the 1968 production, the 1970 film, 2004 performance, excerpts from the Labanotation score, motion capture data, interviews, and photos. This project acts as a best-case scenario when documenting dance and much forethought and planning went into the 2004 revival.

This project leads to my concluding thoughts on the guide as a whole and how you must make do with the resources and time that you have. I have to admit that during my first reading my initial thoughts were to dispute and criticize the tools and practices of the guide and deem them inauthentic. However, in a world of MPLP, budget cuts, backlog, time restraints, and the general difficulties in documenting a dance performance, if all an archive or dance company can do is to videotape a performance and collect supplemental performance materials, then that is as authoritative as anyone else can be. Archival authenticity only asks that the object is what it purports to be, maintains the archival bond, and is truthful about its origins. If the archive is the agent that is documenting performance, then these statements are usually certain to be true. To briefly give an example about digital objects, when you load a digital object it is just the performance of the hardware and software. When thinking about how the United States legal system views authenticity, most evidence is really just deemed “good enough” (such as eyewitnesses, character evaluations, etc.) unless DNA is found.

Unfortunately, I am not certain on how these difficulties will affect promoting dance scholarship and funding for the arts. I did find a number of what I would describe as digital humanities projects when researching the example tools and example projects. I believe that is another way to promote dance and actually display all of the tools being used to authentically document performances and provide access.

My Prezi:

http://prezi.com/cssmy5kknspa/documenting-dance-a-practical-guide/

I tried to utilize the unique display capabilities of Prezi and form my presentation around the word “dance.” My presentation is organized around each letter and uses the space in between. I tried placing each major heading or topic sideways and then going through the topic from the top down. I think it looks interesting aesthetically from far away, but zooms into each section as if it were a single slide. The pathways could also resemble a series of dance movements, further including the dance theme.

TEI Lab Questions

INF385t is encoding letters from the Katherine Anne Porter collection at the University of Maryland, College Park, Special Collections. These are their encoding questions.

Some elements and structures we’ve decided to tag:

Adds < add >
Deletes < delete>
- includes spacing and all small typos
ex. < add> < space n=” ” /> </add >
emphasis (ex. < hi > <emph > w/ rend= “underline”)
Notes in the text:
< note place= “marginLeft” > (or “marginRight” or “interlinear”)
notes taped in, describe in the header under Manuscript Description
Can use < rend align=”vertical” > etc. for any odd placements, etc.
Hand- for major shifts
< handShift scribe= “KAP” medium =”pen” / >
next time it shifts put it back into the previous type use “pen” “type” “pencil”

For Misspellings:
<choice<>sic>budder</sic><corr>butter</corr></choice>
Look up:
Milestones <milestone unit=”part” rend=”+++ “/ >  esp. in context of lines of asterisks, etc.