What Racism? Race and Racism in Recent Canadian Historiography: A Critical Perspective on the Francophone Literature
Bibliographic record
Abstract
Taking the societal climate surrounding issues of race and racism in the study of the past as my point of departure, I propose to examine how it translates into the practice of French-language historiography of Canadian and Quebecois societies. Do disciplinary institutions publish on issues of race and racism? This study also responds to historian Geneviève Dorais’ arguments in the Bulletin d’histoire politique in 2020 about anti-Black racism in Quebec historiography, and to the call by historians Crystal Gail Fraser and Allyson Stevenson for a critical perspective on our discipline in the post-Truth and Reconciliation Commission era. This paper consists of four sections. I first sketch out some definitions related to the concepts of race, racism, and ignorance. I then turn to the relationship these questions have with historical epistemology and the role that the discipline of history has played in the history of racism. In the third section, I present the results of my research on francophone-Canadian historical knowledge production in the past few years, which asked the following questions: Is the concept of race present in this disciplinary field? In what way is it used? Finally, I conclude by returning to the results of this study and reflect on what a better understanding of the critical concept of race in historical studies can contribute to our understanding of Quebec and Canada’s past.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".