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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.062 | 0.013 |
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".