Bibliographic record
Abstract
Abstract Race discrimination is a global phenomenon. Sometimes, the behavior is intentional. In the U.S. context, the Fourteenth Amendment has been interpreted to protect against such activity. In many other circumstances, there is a racially discriminatory impact, that is, the behavior was not intentional. In the United States, the Civil Rights Act covers this type of discrimination. Other countries have grappled with how to handle racially disparate impact as well. The main case selection in this chapter, City Council of Pretoria v. Walker, illustrates how South Africa is handling the concept. That country is emerging from a recent history of de jure segregation against a black majority by the white minority. South Africa has a new Constitutional Court that is interpreting a new post-apartheid constitution with a detailed equality clause that covers both intentional and unintentional behavior. The Walker case implicates issues of race, class, housing segregation, and “reverse discrimination” in interesting ways, and raises the question of how the U.S. Supreme Court would handle a similar case. The notes discuss the experience of the U.S., Brazil, Canada, Europe, and international law.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".