Bibliographic record
Abstract
IS THERE SUCH A THING as a Canadian rights culture?Rights talk in Canada has evolved dramatically in recent history.In the past, Canadians largely defined rights as civil liberties, which meant the rights to free speech, association, assembly, religion, press, due process, and voting.Rhetoric surrounding discrimination as late as the 1950s was largely confined to race, religion, and ethnicity.Today, the language of rights has been appropriated to apply to a remarkable range of issues.Discrimination is banned in human rights law on the basis of race or colour, religion, ethnicity, or national origin, place of origin, sex (including pregnancy), sexual harassment, age, physical and mental disability, marital status, pardoned conviction, sexual orientation, family status, dependence on alcohol or drugs, language, social condition, source of income, seizure of pay, political belief, and gender identity and expression.The rights of Aboriginal peoples and ethnic and linguistic minorities are constitutionally protected.For historians, this is a noteworthy development.Not too long ago, most Canadians would have balked at the idea of criminalizing hate speech or prohibiting sexual harassment at work.This book is both a history of human rights in Canada and an attempt to better understand our rights culture.I attempt to integrate the experience of Aboriginal peoples, albeit this an imposing challenge given the lack of scholarship as well as Aboriginal peoples' ambivalent relationship to human rights in the past.I also draw on sources in French.In part, this is a
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.553 | 0.310 |
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".