Bibliographic record
Abstract
Taking stock of the field of medieval studies in one nationally-defined area has its significant uses. On the one hand, it may be possible to identify specific national characteristics in the research, and perhaps in the future to build upon those strengths and further to emphasise them. Thus, manuscripts and editions (to judge by the SSHRC awards) are an overriding presence for Canadian medievalists. There is acknowledgment of and obeisance to theoretical approaches but the latter tend to be packaged up with one or both of the former, whereas just looking at a set of manuscripts or just embarking on a new edition of a significant text is clearly quite acceptable (with, of course, awareness that all decisions are freighted with the baggage of the researcher’s past and present). Similarly, as a national group we tend to speak with passion about our teaching of matters medieval to undergraduates. More evident, perhaps, in the reports from smaller universities whose remit is a liberal arts curriculum, the desire to communicate well our excitement in our field is endemic, perhaps to all medievalists, and certainly to Canadian medievalists. We fuss, trade numbers of students, recommend texts for reprinting, talk about teaching techniques, and even drag our students (even undergraduates) along to national and international conferences.
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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.079 | 0.057 |
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".