Toward a <em>Sui Generis</em> View of Black Rights in Canada - Overcoming the Difference-Denial Model of Countering Anti-Black Racism
Bibliographic record
Abstract
Throughout this paper, I will use the words "black" and "African-ancestored" interchangeably to refer to people who themselves originated in or whose ancestors in significant numbers originated from the continent of Africa.Just what to call such persons has been the subject of some debate.It has been suggested that the transition from Negro and colored to black and African-American or African-Canadian was as a result of the efforts by persons of African ancestry in the 1960's to achieve a sense of racial pride.See, e.g., F. JAMES DAVIS, WHO IS BLACK?145-46 (1991). A recent survey conducted by the Association of CanadianStudies indicates that when a sampling of Canadians of all races were asked to identify those groups most discriminated against in Canadian society, blacks were typically listed as among the most frequent victims of racial discrimination.As to individual perceptions of racial discrimination by blacks themselves, blacks were the racial or ethnic group who most frequently reported that that they had been discriminated against or treated unfairly by others because of their ethno-cultural characteristics.See Jack Jedwab, Collective and Individual Perceptions of Discrimination in Canada, Report of the Association for Canadian Studies, July 18, 2004, available at http://www.acs-aec.ca/oldsite/Polls/collective.pdf.5. For further discussion of the effects of denying racial difference, see, e.g.,
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.029 | 0.025 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".