Bibliographic record
Abstract
Four years ago Marc Renaud, the ebullient president of SSHRC, appealed to Canadian scholars in the humanities and social sciences to change our ways. Deploring the “death of a thousand journal articles,” most of them attracting at best a handful of readers, he called upon us to “go public or perish.” By “going public” he meant two things: seeking commercial partners and publishing our research electronically. Unless we did so, we were doomed to social irrelevancy and ever-decreasing public support. This year his message is more radical. In his most recent address, “The Human Sciences: The Challenge of Innovation” (available on the Federation website) he urges us to adopt strategies “to survive and succeed in this fast-forward age”: collaborating, especially with our colleagues in the natural and bio-medical sciences, focusing on contemporary problems, and making greater use of leading-edge technologies...in a word, innovating. Those who find Renaud’s analysis persuasive might ask how well Canadian medievalists are meeting this challenge. Those who do not might object that the challenge was never an appropriate one for us in the first place.
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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.038 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".