INTERVIEW: Black Lives Matter—A Discussion with Two Civil Rights Attorneys
Bibliographic record
Abstract
Dr. Martin Luther King Jr. once said, “Human progress is neither automatic nor inevitable . . . every step towards the goal of justice requires sacrifice, suffering and struggle, the tireless exertions and passionate concern of dedicated individuals.” The Black Lives Matter (“BLM”) movement has a formal presence in the United States, the United Kingdom, and Canada. The founders’ outrage at the acquittal of George Zimmerman, who they believed murdered Trayvon Martin in 2013, fueled BLM’s mission to empower Black communities to intervene in the violence inflicted on those communities by both the State and vigilantes and to eradicate white supremacy. Further, BLM goals include “combating and countering acts of violence, creating space for Black imagination and innovation, and centering Black joy to win immediate improvements.”\nThe movement seeks to center on those who have been marginalized by previous Black liberation movements. BLM affirms Black queer and trans individuals’ lives and seeks to move Black communities beyond “narrow nationalism.” It also works to create a world where Black lives are not “systematically targeted for demise” and affirm humanity in all of those facing “deadly oppression.” I reached out to two Black civil rights attorneys to give me their perspective on BLM. Both are alumni of Golden Gate University School of Law. The first, Walter Riley, graduated in 1968 and was recommended by Professor Leslie Rose, and the second, Dewitt M. Lacy, spoke in my Criminal Procedure class about his work as a civil rights attorney.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.047 | 0.008 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.026 | 0.041 |
| Insufficient payload (model declined to judge) | 0.015 | 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".