The Case for Rage: Why Anger is Essential to Anti-Racist Struggle, by Myisha Cherry
Bibliographic record
Abstract
Cherry builds the case for rage that boomed in Audre Lorde’s verse and prose. The Case for Rage delivers a systematic vindication of anger’s essential role in anti-racist struggle, where moral and productive anti-racist anger is named ‘Lordean rage’ after the poet, activist and teacher. The book is incredibly timely, offering the thorough investigation of political anger called for following the extensive uptake of the Black Lives Matter movement during the pandemic. The pandemic has, I think, made this book particularly timely for two reasons: first, for starkly revealing (again) which lives are on the front lines and which are least valued, and secondly, for shutting down most of the distractions that could occupy the time and minds of potential allies. With the world (especially of the privileged) at a standstill, those gripped by profound outrage at the murders of George Floyd and Breonna Taylor were starved for ways to divert or subdue their anger. Many anguished about what to do with their rage. Cherry provides the answer. By combining philosophical rigour with accessible prose Cherry appeals to both ‘academic and activist, the philosopher and citizen’ (pp. 7-8), delivering a methodical treatment of anger that is apt to change not just how we think about the emotion but what we do with it.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.021 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".