A Digital Education

Meredith Dabek, Maynooth University

Category: Assignment 2

Reimagining the Audience for Digital Scholarly Editions

According to the Modern Language Association’s Guidelines for Editors of Scholarly Editions, a scholarly edition’s most basic task is to “present a reliable text,” one that can also contribute to academic research on a particular topic. Traditionally, scholarly editions have had fairly limited audiences, the final printed version intended primarily for other scholars conducting similar research. With the dawn of the digital age, however, the creation of digital scholarly editions is changing the nature of the audience for these works. The availability of scholarly editions online and the use of crowdsourcing to help create these editions are just two ways the digital world is blurring the lines between the traditional academic audience and a much larger, more public audience.

In 2009, at the Association for Documentary Editing Annual Conference, Andrew Jewell presented a presented a paper that explored new ideas around the reading of digital scholarly editions. According to Jewell, “the dominant model for distributing [scholarly] editions in the age of print [was] to sell large volumes at large prices” (1). But the advent of digital publication on the Internet has upended this model by amplifying the reach of a scholarly edition. Where they once would have been available only to a narrowly focused audience, many scholarly editions in digital form can now be accessed by anyone with an Internet connection.

A general audience, however, has different needs than a scholarly one, and may even approach the edition with different intentions. In fact, many casual readers of a scholarly edition may not have even specifically sought out the resource, but rather stumbled across it accidentally. Jewell offers the example of his own Willa Cather Archive, noting that a reader may find the archive “because search engines lead them to hidden bits of knowledge deep in the site” (3). A wider, more diverse audience for a scholarly edition also means the text and content will be consumed in new ways. A printed scholarly edition may follow a traditional, linear format; in a digital world, readers skim, search, scan and skip over parts that may not interest them.

Moreover, readers can access digital editions through any number of Internet browsers, mobile devices or tablets. Each option changes the experience of the edition in subtle ways, even when the content available remains the same. As Jewell correctly points out, “we cannot fully predict how readers will interact with digital publications…[and] we cannot expect every view of that website to be the same for each user” (6). The very nature of the Internet means each visit to a digital edition website will result in a different kind of engagement with the text, with the idea of “the audience” changing each time as well.

The evolving nature of a digital scholarly edition’s audience is not limited to reading and accessing information, though. Some scholarly editions are blurring the boundaries even further by actively involving the audience in the creation of the text itself. In 2010, Cathy Moran Hajo, Associate Editor of the Margaret Sanger Papers, wrote, “Web 2.0 tools are increasing in sophistication and enabling large amounts of people from all walks of life to participate in the creation of editions.” Hajo was, in effect, referring to crowdsourcing and in the years since, an increasing number of cultural and academic institutions have turned to crowdsourcing to complement and contribute to existing projects.

Crowdsourcing in the humanities (or, indeed, in Digital Humanities) aims, in part, to “expand the scope of the community membership beyond academics, and into the interested and engaged general public” (Siemens, et al.). Crowdsourced projects specifically reach out to the audience and invite them into the scholarly editing process, by having them either enrich existing materials or help create an entirely new resource (Carletti et al). In doing so, these projects are not simply looking for free labor, but instead, according to Carletti et al., are “collaborating with their public to augment or build digital assets through the aggregation of dispersed resources.”

Transcribe Bentham, one example of a crowdsourced scholarly edition project, has relied on volunteers to help transcribe thousands of manuscripts from philosopher Jeremy Bentham. The rationale behind opening up this project and scholarly edition to the larger public was due partly because the initiative hoped to “democratize the creation of, and access to, knowledge and humanities research” (Causer and Terras). Beyond opening access to the research, however, crowdsourcing connects passionate, interested individuals with these scholarly projects. The vast majority of crowdsourcing volunteers are not rewarded monetarily, and so many participate simply because they have a deep, personal interest in the subject. And as Ricc Ferrante, Director of Digital Services & Information at the Smithsonian Institution Archives points out, “passion breeds evangelists, breeds new volunteers, and new discoveries,” all of which can, in turn, lead to new knowledge.

There are some who may question the value of an open-access, online digital edition or the use of crowdsourcing to create such an edition. These individuals may maintain that scholarly editions should remain in the realm of the scholar. Ultimately, though, the blurred audience lines can be considered a good thing, as it expands the reach of a particular subject and opens up the humanities to new understandings. For Jewell:

“The defining feature of the broader audience that encounters free, online documentary editions is diversity: it comes from around the world, from a variety of perspectives and educational levels, and with a variety of goals.”

With more diversity comes more readers, more perspectives, and more people discovering new content that they may not have before encountered. Digital tools and technologies create a larger audience for scholarly editions, providing an enriched, varied and dynamic way of accessing and experiencing humanities data. The challenge, then, for scholarly editors, is to “move beyond the ivory towers of research libraries to high schools, town libraries and even to the comfort of private homes” (Hajo). By extending the reach of a digital scholarly edition and blurring the line between a traditional audience and a more expansive one, researchers and editors can ensure that their work is truly open and accessible.


 

Works Cited:

Carletti, Laura, Gabriella Giannachi, Dominic Price, and Derek McAuley. “Digital Humanities and Crowdsourcing: An Exploration.” MW2012: Museums and the Web. 17-20 April 2013. Portland, OR. Paper. Web. 2 December 2014.

Causer, Tim and Melissa Terras. “’Many hands make light work. Many hands together make merry work’: Transcribe Bentham and crowdsourcing manuscript collections.Crowdsourcing Our Cultural Heritage. Ed. Mia Ridge. Ashgate, 2014. 57-88. Web. 2 December 2014.

Ferrante, Ricc (@raferrante). “@McMer314 @sandilo60 @phcostel #askletters1916 …and passion breeds evangelists, breeds new volunteers, and new discoveries = new knowledge.” 2 December 2014, 1:08 PM. Tweet.

Guidelines for Editors of Scholarly Editions.Modern Language Association. MLA, 2011. Web. 2 December 2014.

Hajo, Cathy Moran. “The Sustainability of the Scholarly Edition in a Digital World.International Symposium on XML for the Long Haul: Issues in the Long-term Preservation of XML, 2010. Paper. Web. 2 December 2014.

Jewell, Andrew. “New Engagements with Documentary Editions: Audiences, Formats, Contexts.Library Conference Presentations and Speeches. The Libraries at University of Nebraska-Lincoln, 2009. Web. 30 November 2014.

Siemens, Ray, Meagan Timney, Cara Leitch, Corina Koolen, and Alex Garnett. “Toward modeling the social edition: An approach to understanding the electronic scholarly edition in the context of new and emerging social media.” Literary and Linguistic Computing. 27.4 (2012): 445-461. Web. 2 December 2014.

Text Mining: An Annotated Bibliography

Text Cloud of Text MiningIn 2003, in an issue of the Literary and Linguistic Computing journal, humanities computing scholar Geoffrey Rockwell asked the question, “What is text analysis, really?” More than ten years later, some Digital Humanities are still asking the same question, especially as technological advances lead to the creation of new text analysis tools and methods. In its most basic form, text analysis – which is also known as text data mining or, simply, text mining – is the search for and discovery of patterns and trends in a corpus of texts. The analysis of those patterns and trends can help researchers uncover previously unseen characteristics of a specific corpus, deconstruct a text, and reveal new ideas and theories about a particular genre or author. The following annotated bibliography offers an overview of text mining tools in Digital Humanities, with the intention that it may serve as a starting point for further exploration into text analysis.

Argamon, Shlomo and Mark Olsen. “Words, Patterns and Documents: Experiments in Machine Learning and Text Analysis.Digital Humanities Quarterly. 3.2 (2009). Web. 15 November 2014.

In Argamon and Olsen’s article, they suggest that the rapid digitization of texts requires new kinds of text analysis tools, because the current tools may not scale effectively to large corpora and do not adequately leverage the capability of machines to recognize patterns. To test this idea, Argamon and Olsen, through the ARTFL Project, developed PhiloMine, a set of text analysis tools that extent PhiloLogic, the authors’ full-text search and analysis system. Argamon and Olsen provide an overview of PhiloMine’s tasks (predictive text mining, comparative text mining and clustering analysis), and then summarize three research papers that highlight the tasks’ strengths and weaknesses.

Borovsky, Zoe. “Text and Network Analysis Tools and Visualization.” NEH Summer Institute for Advanced Topics in Digital Humanities. Los Angeles, 22 June 2012. Presentation. Web. 15 November 2014.

This presentation by Borovsky, the Librarian for Digital Research and Scholarship at UCLA, provides an overview of text mining tools, with an in-depth look at a few specific tools: Gephi, Many Eyes, Voyant and Word Smith. Borovsky highlights some of the benefits and challenges of each tool, and offers examples of sample outcomes. Though the slides are presented without the addition of a transcript of Borovsky’s presentation speech, the slides themselves a high-level overview of these four specific text mining tools and Borovsky’s template easily allows readers to discover relevant information about each tool.

Green, Harriett. “Under the Workbench: An analysis of the use and preservation of MONK text mining research software.Literary and Linguistic Computing. 29.1 (2014): 23-40. Web. 15 November 2014.

To help further humanities scholars’ understanding of how to use text mining tools, Green conducted an analysis of the web-based text mining software MONK (Metadata Opens New Knowledge). Green studied a random sample of 18 months of analytics data from the MONK website and conducted interviews with MONK users to understand the purpose of the tool, it’s usability and the challenges encountered. Along with other findings, Green discovered that MONK is often used as a teaching tutorial and that it often provides an entry point for students and researchers learning about text analysis.

Muralidharan, Aditi and Marti A. Hearst. “Supporting exploratory text analysis in literature study.Literary and Linguistic Computing. 28.2 (2013): 283-295. Web. 15 November 2014.

According to Muralidharan and Hearst, the majority of text analysis tools have focused on aiding interpretation, but there haven’t been many (if any) tools devoted to finding and revealing insights not previously known to the researcher. So Muralidharan and Hearst created WordSeer, a text analysis tool designed for literary texts and literary research questions. To illustrate the functionality of WordSeer, Muralidharan and Hearst used this text analysis tool to examine the differences in language between male and female characters in Shakespeare’s plays.

Ramsay, Stephen. “In Praise of Pattern.Faculty Publications – Department of English. Digital Commons @ University of Nebraska-Lincoln: 2005. Web. 15 November 2014.

Ramsay sets out to explore the idea of pattern as a point of Intersection between computational text analysis and the “interpretive landscape of literary studies.” Ramsay wanted to prove that there could be a computational tool that offered interpretive insight and not specific facts or results. So he set out to create StageGraph, a tool designed ostensibly to study structural properties in Shakespeare’s plays, but one also stemming from a branch of mathematics known as graph theory.

Rockwell, Geoffrey. “TAPoR: Building a Portal for Text Analysis.” Mind Technologies: Humanities Computing and the Canadian Academic Community. Ed. Ray Siemens and David Moorman. University of Calgary Press: 2005. 285-299. Print.

In this chapter, Rockwell introduces readers to the TAPoR – the Text Analysis Portal for Research. The TAPoR project began as a collaboration of researchers and projects and eventually proposed a network of labs and servers that would connect and aggregate the best text analysis tools, making them available to the larger academic community. Rockwell then explores TAPoR in more detail, offers an overview of the portal’s specific functions, and discusses the types of users the project envisions will use the tools available through the portal.

—. “What is Text Analysis, Really?Literary and Linguistic Computing. 18.2 (2003): 209-219. Web. 15 November 2014.

In this article, Rockwell argues that text analysis becomes, in effect, an interpretive aid because it creates new hybrid versions of a text by deconstructing and reconstructing some original text. As a result, Rockwell stresses the need for new kinds of text analysis tools that emphasize experimentation over hypothesis testing. He concludes the paper with a proposal for a portal model for text analysis tools, using his own TAPoR as an example.

Simpson, John, Geoffrey Rockwell, Ryan Chartier, Stéfan Sinclair, Susan Brown, Amy Dyrbye, and Kirsten Uszkalo. “Text Mining Tools in the Humanities.Journal of Digital Humanities. 2.3 (2013). Web. 15 November 2014.

Derived from an oral presentation at a research conference, Simpson et al.’s brief article and accompanying poster presents the testing framework developed for the TAPoR text mining tool. The TAPoR testing framework was then used as a proposal for the creation of a systematic approach to testing and reviewing humanities research tools, especially text mining tools.

Text Mining.DiRT Digital Research Tools. n.p., n.d. Web. 15 November 2014.

The DiRT directory compiles information about digital research tools for scholarly and academic use. The directory is divided into several categories, with one category devoted to text mining tools. Users can narrow the category by platform (operating system), cost, whether or not the tool is open sourced and more. Each individual entry includes a description of the tool as well as a link to the tool itself or its developer’s website. While the DiRT directory is an invaluable resource of text mining tools, one drawback is that the tools themselves are not rated in any way, either by the directory’s editorial board or by other users.

van Gemert, Jan. “Text Mining Tools on the Internet.ISIS Technical Report Series. The University of Amsterdam: 2000. Web. 15 November 2014.

van Gemert’s report is a thorough and comprehensive overview of text mining tools available on the Internet, though as it was published in 2000, it is now out-of-date. Still, this report offers a great deal of information both about specific text mining tools and the companies behind their creation. Van Gemert includes website links, summaries and information about available trial versions for each tool.

[Image note: text cloud created from the content of this post using Tagul, an online word cloud creator.]

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