<i>For Whom the Advantage Tolls: Institutional Racism and the Prospective Legacies of</i>SFFA v. Harvard
Bibliographic record
Abstract
Few U.S. Supreme Court decisions in living memory have combined a widespread expectation in verdict with a broad aggrievement of impact as dynamically as SFFA v. Harvard. Anyone remotely concerned with the fortunes of higher education in North America would have had good reason to believe, on or before June 29, 2023, that the “special consideration” of race in university admissions had reached its best-buy date. The key predictive decisions twenty years earlier—Grutter v. Bollinger and Gratz v. Bollinger—tolled the clock. In the Bollinger cases, several justices opined that, a quarter century out, affirmative action policies might no longer be necessary in university admissions to even the score for the racially disadvantaged. The majority in SFFA changed might to must with five years to spare.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.015 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".