International Human Rights Law and Black Lives Matter: Why We Should View Liberation Through the Lens of the Right to Life
Bibliographic record
Abstract
Black Lives Matter.Black Lives Matter is a claim that the humanity of Black people, people of sub-Saharan African descent, should be valued and respected.The phrase, "Black Lives Matter," was coined by Patrisse Cullors in 2013 after the murder of 17-year-old Trayvon Martin by a white vigilante. 1 Together with friends and allies, Opal Tometi and Alicia Garza, Cullors later co-founded the Black Lives Matter Global Network which now has officially recognized branches in the US, the UK, and Canada. 2 In fact, the refrain "Black Lives Matter" was heard globally during protests in 2020, which primarily erupted due to the videotaped murder of a man named George Floyd.In the video, which went viral online, many saw Floyd screaming out "I can't breathe" whilst a police officer kneeled on his neck for over nine minutes. 3 Protests broke out all over the world, from the US to the UK, which is unsurprising as Black Lives Matter is an inherently global
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.101 |
| Scholarly communication | 0.016 | 0.030 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.022 | 0.030 |
| 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".