#SayHerName: Black Women’s Stories of Police Violence and Public Silence
Bibliographic record
Abstract
Fill the void. Lift your voice. Say Her Name.\nBlack women, girls, and femmes as young as seven and as old as ninety-three have been killed by the police, though we rarely hear their names or learn their stories. Breonna Taylor, Alberta Spruill, Rekia Boyd, Shantel Davis, Shelly Frey, Kayla Moore, Kyam Livingston, Miriam Carey, Michelle Cusseaux, and Tanisha Anderson are among the many lives that should have been.\n#SayHerName provides an analytical framework for understanding Black women's susceptibility to police brutality and state-sanctioned violence, and it explains how — through black feminist storytelling and ritual — we can effectively mobilize various communities and empower them to advocate for racial justice.\nCentering Black women’s experiences in police violence and gender violence discourses sends the powerful message that, in fact, all Black lives matter and that the police cannot kill without consequence. This is a powerful story of Black feminist practice, community-building, enablement, and Black feminist reckoning.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.012 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".