From Massive Resistance, to Passive Resistance, to Righteous Resistance: Understanding the Culture Wars from Brown to Grutter
Bibliographic record
Abstract
On this fiftieth anniversary of Brown v. Board of Education,' arriving on the heels of the recent University of Michigan affirmative action cases, 2 I propose taking an "extra-doctrinal" moment to contemplate how the cultural politics of race have likely shaped dominant legal framings regarding society's regulation of access to quality education.This Essay seeks to explain affirmative action jurisprudence as a distinctly cultural phenomenon.My hope is that such a framing will underscore the need for more effective, proactive strategies and broad-based coalitions of diversity advocates.I believe that only such strategies and coalitions can achieve social justice objectives generally, and increase the representation of people of color specifically in university admissions and employment.Although civil rights advocates and critical race theorists hail the Grutter decision as a legal victory for affirmative action and diversity, Grutter is at best a "split decision."The case is most important not as legal doctrine, but rather in its meaning for the cultural politics of race and educational access.3 Justice O'Connor's opinion upholds Professor, DePaul University College of Law.I would like to thank Luke Charles Harris, Kimberl6 Crenshaw, Paulette Caldwell, and Derrick Bell for their support and encouragement in this endeavor.I am indebted to Gil Gott
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.081 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".