Bibliographic record
Abstract
Abstract This chapter develops an understanding of direct discrimination (disparate treatment) and indirect discrimination (disparate impact) in different jurisdictions, and examines the extent to which they can achieve the four-dimensional understanding of equality. Although judges have described direct discrimination as relatively simple, courts have disagreed on some of its basic elements, particularly the role of motive or intention and whether and how direct discrimination can be legitimately justified. In whatever form, it remains limited by its adherence to the principle that likes should be treated alike, for example because of the need for a comparator; and the possibility of responding to a breach by treating everyone equally badly. These issues are explored in Section II. Section III critically assesses indirect discrimination from a comparative perspective across the jurisdictions highlighted in this book and its relationship with direct discrimination. Here, too, it examines the role of intention, how disparate impact is measured, and the standards of scrutiny to justify indirect discrimination, as well as examining the aims and objectives of the concept. Section IV examines ways in which different jurisdictions have relaxed the boundaries between the two concepts, paying particular attention to Canada, the EU, and the ECHR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.075 | 0.021 |
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".