Bibliographic record
Abstract
Thoumi, and Phil Williams.I learned to respect these individuals for their commitment to intellectual integrity rather than to career advancement.Not only is that choice increasingly difficult to make today, but it has become ever rarer to find those willing to try.I want to offer special thanks once more to Phil Cercone and his staff at McGill-Queen's University Press (plus the ever tenacious but still patient Claude Lalumière) for facing the abnormal financial and political constraints normal in publishing today to take on this book as well as seven of its predecessors.My gratitude extends, too, to many people with whom I have had no formal academic or professional association for their encouraging responses to previous books, plus literally thousands of students who listened to me sound off about these issues during a forty-year (so far) university career.For some time now I have made a bittersweet practice of dedicating my books to the memory of certain colleagues much my senior who were a source of inspiration and support during my formative years.Among the things they taught me was not to fear affronting the kind of disciplinary frontiers and professionally approved methodologies that academics, gathered into their usual self-referential cabals, reflexively defend.Whatever its original merits, today the intellectual, professional, and even financial security conferred on academics permits them to climb into their chosen box, lock it firmly
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.302 | 0.232 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".