Bibliographic record
Abstract
The trend towards a “shrinking past”1 is undoubtedly a reality that those of us working in American and Canadian universi-ties have known for many years now. Humanities disciplines, devoted more than others to the study of the past, have suffered inordinately from this situation. The result, one that we as me-dievalists know all too well, is that we end up being the sole departmental expert on the entire pre-1700 (mostly European) world. The reasons for this historiographical downsizing are complex: administrative proclamations of declining interest in the humanities in general are but one piece of the historical puz-zle. There is no need to enter into this debate here, though; what matters is the result. Where there used to be two, three or more medievalists, there is now barely one: a single soul who is often asked to also teach outside the period on more “relevant” issues. So, one it is. But does that make this sole specialist a lonely re-searcher? And is this a novelty?
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.021 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".