Zoning Out Discrimination: Working Towards Housing Equality in Ontario
Bibliographic record
Abstract
In Ontario, it is the role of local government to ensure that housing is accessible and to eliminate barriers to housing. This paper examines how the Ontario Human Rights Code can be employed to challenge municipal zoning bylaws regulating permitted land-uses, namely by establishing that certain bylaws adversely affect individuals protected under the Code by restricting where those individuals may live. While Ontario litigants have been relatively successful in using the Code to challenge direct and indirect discrimination in housing, the case of zoning bylaws reveals key limitations to achieving housing equality through human rights legislation. This paper compares the relative success of legal challenges to bylaws regulating group homes that house people with disabilities to bylaws regulating rooming houses that house people who cannot afford other housing. This comparison reveals the difficulty of challenging discrimination faced by a diffuse group of individuals falling within multiple prohibited grounds (residents of rooming houses), rather than a discrete group that falls under a single identifiable ground (residents of group homes). It also reveals the challenges of confronting discrimination when procedural inequalities are entrenched in municipal decision-making processes. It concludes that the larger challenge for housing and human rights advocates, in addition to eliminating discriminatory bylaws, is to confront systemic discrimination in housing policy and practice. In this task, litigation is a valuable tool but only part of the solution.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.040 | 0.015 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".