Part II: A Cross-Country Checkup <i>A. Universities</i>
Bibliographic record
Abstract
Conceived as a pulse-check for medieval studies in Canada, this project found that the pulse is pounding. Universities that had hitherto not replaced retired medievalists now appear to be so doing. Many of us are expanding or developing courses, and especially programs, in medieval studies at the undergraduate level. The evidence provided in these pages is by no means all positive, but for every lament or conclusion that the bleak future will be odd mentions of our field in classes about medievalism or Harry Potter or Arvo Part, there appears here a paean of praise to new life and new accomplishments. Sometimes, though surprisingly infrequently, these new accomplishments have to do with integrating our work into the new academic world of cultural studies—especially since, as we all know, cultural studies did not start with the first Batman comic. This trend may well accelerate, particularly as we find our balance in this new world of students obsessed by role-playing games, Lord of the Rings, and the effects of religious fervour on human endeavour. At the same time, we seem to be standing firm on the traditional courses which anchor our field, with undergraduate program requirements in Latin or in at least two disciplines, and with introductory courses focused on classic medieval themes or centuries.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 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".