A Word from the President / Un mot de la présidente
Bibliographic record
Abstract
During the year of the year of their one hundred and fiftieth\nanniversary, Britain’s Royal Historical Society’s [RHS]\nreleased a substantial investigation into the state of race,\nethnicity and equality in academic history in the United\nKingdom. Authored by Hannah Arkinson, Suzanne Bardgett,\nAdam Budd, RHS president Margot Finn, Christopher Kissane,\nSadiah Querishi, Jonathan Saha, John Sibdon, and Sujit\nSivasundaram and based on surveys and interviews with 700\nUK-based historians, the report detailed how the staff and students\nin UK university History departments amid a moment\nof enormous demographic and intellectual change remained\noverwhelmingly and markedly white and how racialized –\nhere defined as Black and Minority Ethnic (BME) – scholars\nand students in History departments had “disproportionately\nnegative experience of teaching, training and employment.”\nThe RHS report concludes that addressing these and related\nissues is both “essential for the health of the discipline” and to\n“enhance public understandings of the past.”1 As the one hundredth\nanniversary of the Canadian Historical Association/\nSociété historique du Canada in 2022 nears, it is worth reflecting\non the RHS report and its implications for the discipline\nof history in Canada.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.038 | 0.017 |
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".