Race, Slavery and Justice: A Justice System Case Study
Bibliographic record
Abstract
We do not have to look far today in Canada to see the legacies of slavery in their full effect. One of these legacies is the way in which we have chosen to forget slavery, or perhaps to deny it, and to create a different narrative. “Slavery is Canada’s best-kept secret, locked within the national closet,” asserts Afua Cooper. Ask many Canadians about the history of slavery in Canada and they will talk about the Underground Railroad. This is what many of us learned in school, that slavery existed in America, not in Canada, and that Canada’s heroic, romantic role in that slavery story was to welcome escaping “slaves” from America to freedom in Canada. While there was an “Underground Railroad,” and while it was used to help enslaved persons escape from the U.S. to Canada, that is only a part of our slavery story.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.051 | 0.016 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".