Human Rights Activists and the Question of Sex Discrimination in Postwar Ontario
Bibliographic record
Abstract
Abstract: This article examines the varied understandings of human rights in Ontario in the immediate aftermath of the Second World War. The article compares the social origins and implementation of Ontario's Fair Employment Practices Act – which combatted racist and religious discrimination – with Ontario's Female Employees Fair Remuneration Act – which mandated equal pay for women who did the same work as men. Although a few feminists called for the Fair Employment Practices Act to prohibit sex discrimination as well, their pleas fell mainly on deaf ears in this period. Men and women who fought against racist injustice were frequently unaware of gender injustice, for they, like so many others, subscribed to the deeply embedded ideology of the family wage. Conversely, some of the most outspoken advocates of women's rights were unconscious of – or chose to ignore – racism. At the same time, some of the most committed advocates of equal pay for equal work actually reinforced certain conventional assumptions about men's gender privilege at work and at home. Moreover, while the enforcement of both acts was constrained by the conciliatory framework embedded within them, the government officials who were charged with applying both acts interpreted the equal pay act quite narrowly and were significantly more diligent in tackling racist and religious employment discrimination.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".