Adverse Effects Discrimination
Bibliographic record
Abstract
Legal writers refer to this situation in Canada as “adverse effects discrimination” (Yorke, 2016), in the U.K. as “indirect” discrimination (Priest v. Canada, 2022), and in the U.S. as “adverse impact discrimination” (R. v. Sharma, 2022) or “adverse discrimination effect” (Regehr, Kanani, McFadden & Saini, 2016, p. 36). These terms describe a policy intended to treat everyone equally, but that ends up harming a protected group. One of the most notable cases in legal history was Gosselin v. Quebec (Attorney General), which challenged parts of Quebec's social welfare scheme that provided lower social assistance rates to individuals under 30 who did not participate in retraining programs. This court decision largely hinged on “The Law's Test”. This chapter offers an overview of various opinions published about this case. Gosselin is part of a needs assessment for judicial guidance in deciding equity cases. Other examples of age-based discrimination are also discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".