Scholarly Discourse and Computing Technology II: Perspectives on Pedagogy, Research, and Dissemination in the Humanities
Bibliographic record
Abstract
The following essays have evolved from papers given at conference sessions held during the 1998 Congress of the Social Sciences and Humanities at the University of Ottawa, Canada, and jointly sponsored by the Consortium for Computers in the Humanities and the Association for Canadian College and University Teachers of English. These papers continue an exploration that the two associations have, over several years of aligned sessions, pursued together — an exploration that is given voice in earlier collections that we have had the pleasure of overseeing: Technologising the Humanities / Humanitising the Technologies (from the 1997 sessions, published in electronic form by Computing in the Humanities Working Papers [September 1998] and, in print, by Text Technology 8.2 [1998]: 1-63 and 8.3 [1998]: 1-76) and Scholarly Discourse and Computing Technology: Perspectives on Pedagogy, Research, and Dissemination in the Humanities (from the 1996 sessions, a similar joint special issue of the print journal Text Technology 6.3 [1996]: 137-216 and the electronic journal Computing in the Humanities Working Papers [April 1997]).
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 | 0.034 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.021 |
| Science and technology studies | 0.019 | 0.082 |
| Scholarly communication | 0.064 | 0.044 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".