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Professor Johanna Drucker’s book, SpecLab: Digital Aesthetics and Projects in Speculative Computing, brings to the forefront the interpretive, emergent, situated, subjective, and aesthetic positions and acts involved in knowledge production in the field of digital humanities. The book describes projects Drucker and other researchers undertook at SpecLab at the University of Virginia from 2000 to around 2008.

This book contains many ideas to digest, ruminate about, and practically apply to digital humanities projects. Several definitions and descriptions are deepening my understanding of digital humanities and speculative computing:

“The field of digital humanities is not simply concerned with creating new electronic environments for access to traditional of born-digital materials. It is the study of ways of thinking differently about how we know what we know and how the interpretive task of  the humanist is redefined in these changed conditions.” (xiii)

and

“The event of interpretation in a digital environment includes many steps: creating a model of knowledge, encoding it for representation, embodying it in a material expression, and finally encountering it is a scene of interpretation.” (xiv)

Drucker is concerned with knowledge and its representation through digital means, which involves conscientious consideration and employment of both praxis and theory (hack and yack). For example, when creating a digital surrogate of an existing text, the practical choices made, such as whether the text will be scanned (creating a facsimile) or keyboarded (which may cause the surrogate to lose some of the original formatting), concomitant with the metadata and HTML/XML schemes used to categorize and display the digital surrogate, have theoretical significance and implications – the practical choices effect how the digital surrogate will be interpreted and understood.(6) Drucker states: “…the visual form in which information is presented has a great impact on how that information reads and what it is assumed to communicate.” (74)

Another concept discussed in various ways throughout the book is the performative aspect of a digital environs. For example, metadata schemes, programming codes and protocols, and markup tags in combination “do” something, meaning, a new reality is created and brought into being each time the digital environment is accessed.

I find this book wildly evocative and challenging theoretically and practically. One week was not enough time to assimilate the concepts and ideas, and I look forward to spending much more time with it.

Representation: The action of standing for, or in the place of, a person, group, or thing, and related senses. Something  which stands for or denotes another symbolically; an image, a symbol, a sign.

Re-presentation: Presentation of a person or thing for a second or further time; an instance of this.

(Oxford English Dictionary)

This week I have been thinking a lot about my word for our final project: representation. Which has led me to think about its closely aligned neighbor, re-presentation. Often this week, these two words became elided in my mind. (For blog readers not in dh201, our class is collaboratively building an online encyclopedia of words related to the digital humanities. Each of us has chosen a word to explore, interrogate, define, and represent (!) through text, images, video, graphics or audio.)

How do I represent (re-present) representation in our encyclopedia?

I have been thinking about the contexts (places, ideas, objects, people, phenomena) in which representation manifests itself. So many of our day to day objects are representations of or for something else: a map or a dance can represent points in space and geometry; a piece of music can represent math, physics, and emotion; a painting can represent shape, line, perspective, and emotion; a book can represent authorial and publisher intent.

In the digital humanities, representation is closely aligned with the word “model.” According to Williard McCarty, a model is “either a representation of something for purposes of study, or a design for realizing something new.” And then there are of course, computers, which are “not so much engines of computation as venues for representation,” [italics in original] which when used to model or re-present an artefact or phenomena provides the opportunity for new perspectives, understandings, and interpretations about the artefact/phenomena to emerge.

And there is another form of representation, evinced through cultural protocols:

The database Digital Dynamics Across Cultures, (a database of photographs and videos documenting the stories and places of historical importance to the Warumungu community located in Tenant Creek, Australia) is a precursor to the content management system, Mukurtu, and a project in the Vectors Journal, Ephemera. Besides containing digital representations of the Warumungu community’s cultural artefacts, the database also represents (and digitally re-presents) the community’s cultural protocols concerning the distribution, viewing, and reproduction of their cultural knowledge through limiting access to images or information in harmony with their beliefs.

This blog entry has only scratched the surface in defining and interrogating the ways in which representation and re-presentation can be manifested in a digital environment…to be continued.

 

How historical evidence is considered, constructed, represented, and transmitted in historical practice (by historians/researchers) and experienced (by end-users) is a common theme in this week’s class readings and by extension, in the two websites.

In Deconstructing History, Alun Munslow discusses the constructed-ness of the historical narrative. He is a supporter of historian Hayden White’s idea that the historical narrative is not something that “preexists” and is discovered by the historian, but rather, it is the historian who invents and constructs the historical narrative. Furthermore, the historian’s interpretation of historical evidence also relies upon and is determined by the structure and narrative of  the archive where the historian got his/her information – who assembled the archive, why, and what has been included or excluded? All of this points to the multi-layered construction of the historical narrative, the multiple and diverse “pasts” of history and the complexity of interpreting past events.

Stefan Tanaka also writes about the use of narrativity (specifically linear chronological narrativity) in historical practice in Pasts in a Digital Age. He describes how in the late 18th century a “specific form of historical thinking emerged” (promoted by Leopold von Ranke) where the past started to be written about using a linear narrative structure where “chronological time, not place, community or environment” was how the past was understood. There were those that opposed this shift in historical writing, such as Thomas Carlyle, who stated: “Things are done in a group, not in a series.”

Tanaka is not necessarily opposed to the chronological narrative, and believes it has its place and purpose (it helped synchronize time and events and at the time gave order to all of the new data which was emerging and that needed to be “defined, collected, and organized.”) One of Tanaka’s interests is to interrogate the effect of digital media on our relation to and understanding of the past and of the future, an interrogation coupled with his belief that history could benefit from an “understanding that other forms of socio-temporal modes of organization did, and do exist” and that “understanding is not the accumulation of data, but of locus, relations, and connections.”

Both of the websites assigned for this week, The Valley of the Shadow: Two Communities in the American Civil War, and Digital Harlem, do focus on “locus, relations and connections” and broadened my understanding of the historical pasts represented in the two sites. Both sites provide interesting ways to relate, connect, and spatially place historical knowledge. For example, The Digital Harlem site allows one to layer diverse events, places, and people on points on a map, offering interesting ways to see and consider time, space, events, and people.

The Valley of the Shadow, although quite chronologically based, provides novel ways to examine related historical events (the eve of the American Civil War, the war years, the aftermath) through historical records from that era (newspapers, records, census and tax records, diaries, maps, church records, etc.) not just by themselves, but through a relational-spatial interface that allows simultaneous and comparative views of the records from the North and from the South, which when looked at together, provide interesting views on what life was like (on both sides) during the Civil War era.

These two databases afford the end-user the chance to explore multiple historical narratives, help us reformulate history so that we might recover some of the complexity of human activity and demonstrate the importance of looking at historical phenomena in groups – of objects, time, people and places – as opposed to just looking at these things in a series or strings of events in order to gain multifarious insights and generate new knowledge about the past.

My topic modeling dataset was a set of abstracts (total of 373) from the 1996-2003 annual conferences of the Alliance of Digital Humanities Organizations. (The Digital Humanities Conference).  Running the topic modeling tool, which automagically extracts topics from texts, for 20 topics, 200 iterations, and 10 words resulted in the list below. In parentheses is my topic name or the definition of what I think that topic is about.

For some reason, the process of defining/naming each of the 20 topics was not an easy task for me. Questions abounded, such as: Do I need to take all 10 words in a topic into account, meaning, do all ten words need to be reflected in the topic’s name? Do some words in a topic have more weight, or meaning, than others? (In class Zoe Borovsky mentioned that the words at the front of the list are the ones most frequently appearing in that topic.) So, should my topic name reflect the meaning of the first few words only? What about topics that seemed to be about two or more things? Can topics have sub-topics?  It became clear to me how subjective and interpretive this process is. Professor Posner helped clarify this task for me: “If we were trying to write a publishable paper, we’d do a lot of checking back and forth between the text and the topic; but what we’re doing is just a gesture toward classification, not a hard-and-fast organizational system into the naming/defining of these topics.” This helped me realize that having a clear idea from the outset of why one is doing the extracting – what is to be learned and accomplished from the task of extracting – is very important.

The twenty topics and their names/definitions:

1. time process work years case forms large found important form (work process – length of time)

2. knowledge historical form representational social meaning tradition significant level cultural (historical knowledge – social, cultural significance/meaning) – This one was difficult to interpret and name. Scanning some of the actual abstracts found the following, which helped somewhat:

  • personal and cultural forces shape how people organize information
  • structural theories of reading
  • user cultures, literacies and conventions
  • historical ways time and temporality have been conceptualized
  • community in an online context
  • philosophical analysis of “representation” and “interpretation”

3. de la des les le du une en dans pour (foreign language words – of, the, a)

4. project images image history work projects design ptr materials scholars (history of image projects)

5. tools xml html target http texts text display based (tools used for display of texts)

6. order language information rules semantic based structure analysis common languages (analysis of language)

7. user system web software users interface internet material make delivery (information system)

8. information documents document research text data xml literary retrieval field (retrieval of text documents)

9. humanities computing research university resources technology teaching development support community (humanities computing research in academia)

10. gt It sgml markup tei document encoding dtd documents elements (encoding and markup of documents)

11. data emph rend italics terms features frequency components fragments component (data components and features)

12. text textual texts literary theory hypertext reading encoding writing critical (theory -reading and writing)

13. text word texts word number order line amp context set (words and texts/context)

14. electronic edition editions manuscripts publication manuscript scholarly university project book (electronic edition academic book)

15. analysis words texts authorship study style studies authors middle statistical (analysis of writing style of authors)

16. model paper set approach specific problem problems features work level (approach for modeling a specific problem)

17. language corpus linguistic english annotation corpora data speech analysis linguistics (English language corpus)

18. digital data information database library metadata collections objects research databases (research in/of digital library databases or library collection or metadata for digital database)

19.  dictionary information verb system entries entry dictionaries translation form syntactic (dictionary entries)

20. students learning student writing multimedia technology web group create courses (students learning multimedia technology)

Confusion with TopicsinDocs.csv

I went to the TopicsinDocs.csv list for this same run of  20 topics, 200 iterations, 10 words.

Scanning the TopicsinDocs.csv list I became interested in documents/abstracts that only contained one topic. For example, document/abstract 30 has one “top topic,” which is topic 3. This topic makes a 92.1% contribution to the document/abstract.

I then went to theTopic Index – List of Topics (html), to find out the word clusters in this topic, which are “de la des les le du une en dans pour.” (topic 3)

Clicking on this word cluster-topic led me to a list of the top-ranked documents/abstracts in this topic (#words in doc assigned to this topic). I clicked on document/abstract #30 to see what more I could find out about “de la des les le du une en dans pour” in the context of this document/abstract.

At the actual document/abstract, I am given the file name, DOC :2000_paper_662_loiacono.txt., the title of the document/abstract, “Primroses and Power: a Study on Linguistic Excellence in Political Discourse” and the document/abstract itself. Scanning the document/abstract, I did not find any of  the words “de la des les le du une en dans pour.”

Furthermore, this page contains a list: “Top topics in this doc (% words in doc assigned to this topic).”  The topic,“de la des les le du une en dans pour” is nowhere to be found in this list.

I tried another run of this. Document/abstract 66 also only contains this one topic, “de la des les le du une en dans pour” at 93.8%. I went through the same process – ending up at the document itself, DOC :2002_paper_110_gabler.txt, “There is Virtue in Virtuality. Future Potentials of Electronic Humanities Scholarship.” Again, I did not see “de la des les le du une en dans pour” in the document/abstract itself, nor was it listed as a “Top topics in this doc (%words in doc assigned to this topic).” I am thoroughly confused with this.

Different iterations

I ran the topic modeling tool for 5 topics, 200 iterations, and 10 words, and then another run for 5 topics, 2000 iterations, and 10 words. I was interested to compare and contrast different levels of iteration to see if a higher iteration would yield more refined or nuanced results. There wasn’t that much difference between the runs. The 200 iterations and 2000 iterations results were very similar in regards to the word clusters found in the topics and the order of the list of the 5 topics:

1. language corpus word information english linguistic based dictionary data system (200)

1. language corpus word analysis texts words text english linguistic data (2000)

2. humanities project research university digital students web electronic computing information (200)

2. humanities digital research project students university web information computing resources (2000)

3. text gt It document sgml xml data encoding markup documents (200)

3. de la des les le authorship style de une authors (2000)

4. texts texts analysis literary work words study studies theory reading (200)

4. text electronic texts literary textual hypertext edition writing computer (2000)

5. de la des les le du du une en dans pour (200)

5. gt It text document sgml data xml encoding markup documents (2000)

Conclusion

Although my experience with using this topic modeling tool was often frustrating, and left me with more questions than answers (perhaps that is the point),  I am definitely interested to keep working with it to examine  large bodies of texts related to the arts, such as dance reviews from the New York Times, scholarly articles from PAJ: A Journal of Performance and Art or Movement Research Performance Journal.  The ability to distantly read large swaths of historical or contemporary conversations in dance and theater in order to discover something new about the conversations or to discover patterns in regards to themes, ideas, events, people etc., is exciting to me. I am also interested to learn how to use a modeling tool for graphic objects such as labanotation scores in order to analyze changes in modern dance choreography throughout the years. Lastly, I am curious to know if there is a tool that exists to topic model (movement model?) choreography on film or video.

In one of last week’s class readings by Natalie Cecire, When Digital Humanities Was in Vogue, Cecire posed a question that resonated with me: “What is the moral and political force of digital humanities—what are its cultural and institutional consequences?” As a performing arts librarian, archivist, and artist working with digital collections and on digital projects outside of the digital humanities discipline, I am deeply interested to incorporate digital humanities tools, practices, and theory into my work, which for me, both personally and professionally, means finding answers to Cicere’s question.

Helpful on this quest for answers is the Software Studies Initiative’s web site which contains the slideshare How and why study big cultural data by Lev Manovich and a TED talk by artist Aaron Koblin. In the slideshare, Manovich makes the case for studying and using big cultural data:

  • in order to find a more inclusive understanding of history and present (using much larger samples),
  • to detect large scale cultural patterns, and
  • to map cultural variability and diversity

In regards to discovering a more inclusive understanding about historical or present phenomena through big cultural data, I am reminded of Tim Sherrat’s article, It’s All About the Stuff: Interfaces, Power, and People. In this article Sherrat details the process of creating the database, the real face of white australia, from digital copies of documents generated by the Commonwealth of Australia’s “White Australia Policy,” a policy enacted in the early 1900’s that restricted and monitored the lives of people because of the color of their skin. These records are housed physically and virtually at the National Archives of Australia.

Through the marriage of big archival data and technological tools Sherrat was able to extract portraits from a large volume of archival records (digital copies of the certificates – which include portrait photographs – that non-white residents had to use in order to leave and return to Australia) related to the White Australia Policy. the real face of white australia not only presents a new way to experience and understand archival records and an historical phenomenon, it also becomes a “moral and political force” by  its inclusivity of, and bringing to the fore, the people whose lives were captured in these documents.

In the TED talk, Artfully Visualizing our Humanity, artist Aaron Koblin, who specializes in data and digital technologies explains the concepts behind and building of his visual and aural art projects, which exemplify many of the ideas found in Manovich’s slideshare. A large number of his projects are produced collaboratively (crowd-sourced) and with community generated data in order to show and reflect upon large scale cultural trends, and to tell stories.

Koblin’s thought provides one possible answer to Cicere’s question (I believe there are many answers to her question):
“What is the moral and political force of digital humanities – what are its cultural and institutional consequences?”:
“To maintain our humanity and tell some amazing stories.”

At their core, all of the class readings this week seem to (either explicitly or implicitly) beg the question: What is Digital Humanities?

From which follows further questions:

  • How much of digital humanities is practice or (T)theory or a combination of the two?
  • Who gets to sit at the digital humanities table? Hackers? Yackers? Hackers who yack? Yackers who hack? Individuals who may not align themselves with the nomenclature, hacker or yacker?
  • Who decides who gets to sit? Those inside the academy? Those outside the academy?
  • Which types of “Theory” (literary, critical – the academic canon) or “theory” (1) generated from a rigorous investigation of one’s practice (2) embedded in a material object that serves to critique or is itself a critique of some phenomenon, should be employed or considered? And why?

Regarding hacking and yacking, in A Companion to Digital Humanities, Schreibman, Siemens, and Unsworth state that digital humanities uses “information technology to illuminate  the human record, and bringing an understanding of the human record to bear on the development and use of information technology.”  Hence hacking – the ‘doing’ i.e., the making of something such as a database or archive (which can also involve reflective writing about the process of ‘the doing’ in order to illuminate what was learned and which could also be useful to others) is as important as (T)theory – yacking.

Regarding the conversations in many of the articles about the tensions between the ways “Theory” and “theory” are used (or not) in digital humanities: could these tensions be considered paths to new possibilities of engagement for the digital humanities?

Moya Z. Bailey asks a question I too have been pondering: What counts as a digital humanities project?  She offers several examples of people engaged in digital humanities work within academia (but not in the digital humanities sector) and outside of academia whose socially and critically engaged projects fall outside of “mainstream digital humanities.” Which brings me back to my question – who gets to sit at the digital humanities table, and who decides this? Bailey offers propitious advice: “In re-imagining what counts as digital humanities, we can draw on the wisdom of scholars who have addressed related issues in their own fields of study.” And, “Work that is already aligned with the digital humanities and perhaps even pushing the field in new directions should be celebrated and sought out.”

Concurrently, Alexis Lothian, a founding member of the #transformDH collective (a collective engaged in writing and digital humanities projects that critique the status quo – culturally, socially, politically) is thinking along the same lines: “What conversations, art forms, databases, and archives might do the work of a transformative digital humanities, though they lack institutional status to be named as such?”

This seems like a good place to end this blog, and more importantly, start new conversations about What is Digital Humanities?

 

Two of our class readings this week, Topic Modeling for Humanists: A Guided Tour by Scott Weingart and Radiant Textuality by Jerome McGann brought me back to thinking about how, and the places where, dance and the digital humanities meet. McGann, in “Rethinking Textuality” (chap. 5) provides a plethora of ideas about the attributes and behaviors of texts: they comprise signifying parts, both graphic and semantic; they are records of their own history; they are dynamic and generative; they are networks. His ideas about the attributes and behaviors of texts could be extrapolated to describe choreography: movement phrases as texts; a dance as a document.

Throughout the chapter, McGann continues his contemplation of the text: “Text generates text, it is a code of signals elaborating itself within decisive frames of reference. A text is a display and record of itself, a fulfillment of its own instructions. Every subsequent re-presentation in whatever form – editorial or interpretive – ramifies the output of the instructional inheritance. Texts are like fractal derivations.” (p. 151)

This leads me to William.

Synchronous Objects, a re-presentation of choreographer William Forsythe’s dance, One Flat Thing, reproduced, contains multiple derivations (The Objects) of the dance’s movement phrases-texts, revealing inherent instructional structures, shapes, and patterns which would not be discernible without technology. Thus, the movement phrases-texts are extended and amplified, offering new ways to experience, study, and share a dance-document. The Object, Movement Density, is a good example of this.

Regarding topic modeling, the Object, MovementMaterial Index, (see “Information Graphics 1, 2, 3, 4”) is perhaps an example or form of topic modeling in dance. Information Graphics 1 and 4 seem to most resemble topic modeling in DH as they both visualize patterns and counts of movement occurrence, repetition, and stillness. Further, the data from the Information Graphics 1, 2, 3, 4,  were analyzed, resulting in the identification of 6 movement sequences which were subsequently termed “themes” (a.k.a. “topics” in DH).

One of the Creative Directors of Synchronous Objects, Maria Palazzi, in writing about Synchronous Objects, sums up well these thoughts on textuality, dance, and technology: “We could, after all, read this dance.”