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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".