Why I Killed Canadian History: Conditions for an Anti-Racist History in Canada
Bibliographic record
Abstract
Anti-racism provides the basis for a richer understanding of the past, an understanding that is potentially more sensitive to the requirements of generally accepted standards of historical criticism than is the nationalist framework that shapes most historical writing about Canada. An anti-racist history takes seriously the existence of racisms and asks questions about their roles in shaping institutions and experiences, including those of dominant groups. It encompasses previously excluded meanings through a broader understanding of the historical record: written, oral, and material. It views the rise of nationalism and nation-states within the larger context of European colonialism, transforming nationalist projects (such as the making of Canada) into historical problems to be explained, rather than taking them for granted as organizing devices for the study of the past. It allows questions to be asked about how some identities come to be seen as fixed, how certain ones become normalized and others marginalized. Anti-racism thus has the potential to develop a better history than the nationalist one whose loss is lamented by J. L. Granatstein in Who Killed Canadian History?.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.069 | 0.026 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".