Bibliographic record
Abstract
Dene Grigar is Professor and Director of The Creative Media & Digital Culture Program at Washington State University Vancouver whose research focuses on the creation, curation, preservation, and criticism of Electronic Literature, specifically building multimedial environments and experiences for live performance, installations, and curated spaces; desktop computers; and mobile media devices. She has authored 14 media works such as "Curlew" (2014), "A Villager's Tale" (2011), the "24-Hour Micro E-Lit Project" (2009), "When Ghosts Will Die" (2008), and "Fallow Field: A Story in Two Parts" (2005), as well as 56 scholarly articles and three books. She also curates exhibits of electronic literature and media art, mounting shows at the British Computer Society and the Library of Congress and for the Symposium on Electronic Art (ISEA) and the Modern Language Association (MLA), among other venues. With Stuart Moulthrop (U of Wisconsin Milwaukee) she developed the methodology for documenting born digital media, a project that culminated in an open-source, multimedia book, entitled "Pathfinders" (2015), and book of media art criticism, entitled "Traversals" (2017), for The MIT Press. She is President of the Electronic Literature Organization, Associate Editor of "Leonardo Reviews," and "Literary Studies in the Digital Age (LSDA)," and a series editor for "Electronic Literature," with Bloomsbury Press. In 2017 she was awarded the Lewis E. and Stella G. Buchanan Distinguished Professorship by her university. She also directs the Electronic Literature Lab at WSUV. In her spare time she runs, collects wine, does yoga, and misses the Gulf Coast.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".