The Ontario Human Rights Code’s Distributive and Recognitional Functions in the Workplace
Bibliographic record
Abstract
In her analysis of the purpose of the Ontario Human Rights Code, the author draws on Nancy Fraser’s distinction between the two main strategies that have been used to combat inequality. Strategies of redistribution, which prevailed among equality activists in the early twentieth century, see inequality as arising from unequal access to economic resources. Strategies of recognition, which have come into prominence more recently, see inequality as arising from sociocultural prejudices that deny equal recognition to disadvantaged groups. Although the Ontario Human Rights Code is often seen as focusing on recognitional issues, the author argues that through the market relationships the Code regulates and the remedial powers it grants, it also adopts a redistribution strategy designed to address the economic impact of prohibited discrimination: that is, the Code aims to change how resources and opportunities are to be allocated for those with protected identity traits. An understanding of the interaction between the Code’s recognitional and redistributive functions sheds light on its purpose and method of operation, as well as on its relationship to other equality-seeking legal mechanisms such as collective bargaining and the equality rights provisions of the Canadian Charter of Rights and Freedoms. Thus, the need for a range of legal tools to counter inequality in different contexts comes more clearly into focus.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.022 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".