What racism? : an exploration of ideological common sense justifications of racism among educators in Quebec English-language education
Bibliographic record
Abstract
This study starts with the observation that Canadians un-self-consciously tend to understate, or fail to recognize, the existence and extent of anti-Black racism in Canada. Canadians also claim that racism is much worse in the United States. Using extensive excerpts from in-depth interviews with Black and White educators in the Quebec English-language school system, the study examines ideological common sense arguments that legitimize, or else, argue away Canadian anti-Black racism. The study also documents the participants' accounts of racism and its effects. The study exposes arguments used to deny and justify racism, and discusses the disparate understandings of race-related concepts that make it difficult for dominant and oppressed racial groups to see eye-to-eye. The author then uses the findings of the study to answer and critique a 1998 article by S. Davies and N. Guppy that challenges the claim that there is anti-Black racism in Canadian education. The final chapter of the study suggests that the American literature on race is more relevant to the Canadian context than is often acknowledged. It suggests that anti-racist education in Canada has less to do with "giving teachers...strategies" for passing on "tolerance to the next generation" than with teaching teachers to examine their own assumptions. The author recommends that Canadian education be examined through a Critical Race Theory approach, which centers race.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.041 | 0.029 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".