Bibliographic record
Abstract
The United States recently saw the largest racial justice protests in its history. An estimated 15 to 26 million people took to the streets over the police killings of Breonna Taylor, Tony McDade, George Floyd, and countless other Black people. This Article explores how these protests and their chants of “no justice, no peace” should lead us to reconsider American equality law. The Article surfaces legal claims—here called “peace-justice claims”—that address the relationship between ameliorating inequality and achieving peace. Using unpublished archival documents, it tells the story of how Americans embroiled in early desegregation debates sought competing visions of peace that either included or excluded justice. Furthermore, it demonstrates how the Supreme Court’s landmark decision in Cooper v. Aaron arbitrated those claims in favor of integration. The Article also traces how those claims have evolved and how the Court has used peace and justice considerations to limit rather than advance minority rights. This analysis shows that intertwined arguments about justice and peace lie at the heart of equal protection doctrine. Using sources of both legal and social history to identify peace-justice claims, this Article contributes to a “new civil rights history,” expanding the scope of legal actors beyond lawyers and judges to include policymakers, social activists, and lay people. Juxtaposing minority claims with court-developed legal doctrine highlights the Supreme Court’s inadequate recognition of the peace-justice interests at stake. Proposing “no justice, no peace” as a corrective to the law, this Article argues that courts should recognize the exclusion and estrangement of Black people as a basis for minority-protective interpretations of the Constitution. This attention to peace-justice claims is enriched by insights from transitional justice, a field that helps societies to overcome histories of oppression. Although societies require both peace and justice, these values sometimes appear in tension, leading to what is internationally known as the “peace versus justice dilemma.” Viewing legal cases as sites of this dilemma draws attention to whether American courts seek a “negative peace” based on the suppression of social conflict or a “positive peace” grounded in the pursuit of social justice. This Article demonstrates why and how American law and legal institutions should strive for positive peace by addressing structural inequalities.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.042 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".