Bibliographic record
Abstract
Could a woman be a historian? Editors Beverly Boutilier and Alison Prentice pose the question in their introduction, prompting us to read the ten essays in this collection as answers, and as explorations of those two socially constructed terms woman and Affirmative answers, needless to say, since this is a collection about the creation of historical by historians who, as and Canadians, were themselves making history. Yes, all ten essays suggest, women could be and were historians, before proceeding to illustrate just what kind of historians one found making history in Canada between 1870 and 1970. This is not a history, book; rather, it is a collection of essays about the making of history, a historiography. As such, it is a significant addition to the shelves of those interested in Canadian history, as well as Canadian studies, and women's and gender studies. As a collection of ten articles by both men and women, and edited by two Canadian historians of different generations, this collection illustrates - in content and method - the range and depth of historical memory making in English-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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".