Mind Technologies: Humanities Computing And the Canadian Academic Community
Bibliographic record
Abstract
In recent years, the application of computing technology to the arts and humanities has been a topic of increased focus in the post-secondary environment. With growing understanding of how these applications can serve the ongoing mission of humanities research, teaching, and training, technology is playing a larger role than ever before in these disciplines. Arising in part from a joint venture between the Consortium for Computers in the Humanities / Consortium pour ordinateurs en sciences humaines (COCH/COSH; now SDH/SEMI, the Society for Digital Humanities / SociA (c)tA (c) pour l'A (c)tude des mA (c)dias interactifs) and the Social Sciences and Humanities Research Council (SSHRC), Mind Technologies: Humanities Computing and the Canadian Academic Community is the first volume to broadly document the internationally significant work of the Canadian academic community in the area of humanities computing. With Contributions By: Michael Best John Bonnett Susan Brown Alan Burk Terry Buttler Lisa Charlong James Chartrand Charles Clarke Patricia Clements Renee Elio Natasha Flora Paul Fortier Scott Gerrity Robert Good Sean Gouglas Nicholas Griffin Isobel Grundy Ian Lancashire Peter Liddell Karen McCloskey Murray McGillivray Andrew Mactavish France Martineau David Moorman Aimee Morrison Stephen Reimer Geoffrey Rockwell Ray Siemens Stefan Sinclair David Strangway Elaine Toms Christian Vandendorpe Russon Wooldridge
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.039 | 0.018 |
| Scholarly communication | 0.027 | 0.010 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".