Bibliographic record
Abstract
In theorizing the digital text, I will take a two-pronged approach: a) what aspects of reading cannot be accounted for by the types of digital textual analysis done so far in the digital humanities, and b) how can technology (be “used” to) account for such possibilities? To answer the second question, we need to stop seeing the computer as a “means” (i.e. we “use” a computer) and to start thinking about the computer itself as a part of the literary process. This is perhaps to blur the distinction between e-literature and media studies on the one hand, and digital humanities on the other. However, it presupposes that technology is not something to be feared (as “tampering” with the text), but that it is rather something intrinsic, to be conceived on its own terms. Indeed, the computer can enhance the literary experience and highlight aspects of the text that were not noticed before, and vice versa, in a sort of feedback circuit, bringing with it hermeneutic questions that hitherto have been only indirect. What might we discover from exploring the symbiotic relationship between the text and the machine and about the minds and bodies that encounter these? Such encounters occur not only through visualization, but through sonorization and through the body. Such hybrid encounters require a broader view of language than that provided by information theory, which has apparently dominated digital literary studies. I will use my own digital humanities project on the visualization of French poet Stéphane Mallarmé’s works (http://mallarme.uvic.ca) to explore models of reading the digital.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".