Standards of Scrutiny, Equal Protection, and Illicit Motives for Discrimination
Bibliographic record
Abstract
Abstract Connection between anti-discrimination law and the ideal of public reason (PR) is found in a doctrine that the invidious quality of a law claimed to be discriminatory must ultimately be traced to discriminatory legislative motives/purposes. This chapter begins by taking seriously a concept of ‘suspect classifications’ developed in the US Supreme Court’s case law under the Fourteenth Amendment, and inquires into the most likely features that may render a classification ‘suspect’, which inevitably—as the chapter argues—leads to the question of legislative motivations behind a legal classification. The chapter offers a theory which may help us understand the idea of ‘prejudice’ by looking at external ‘indicia’ of such prejudice, hence, of suspect classifications. Illustrations for this proposition are taken from the US and South Africa. Finally, using examples from India and Canada, the chapter offers some examples of how judges figure out the presence of impermissible motives behind a legal classification.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".