Bibliographic record
Abstract
Anger-eliminativism, the view that we should, as much as possible, reduce the role anger plays in our moral lives and theories, fails in ways predictable of anti-intersectional methodologies. In failing to adopt intersectionality as a maxim of inquiry, anger-eliminativism ignores, dismisses, and misrepresents the angers of those who have clear and pressing moral reason to be angry—namely, those who face oppression. It is also problematically a priori at various levels of inquiry, insensitive to counterexamples, and begs the question of anger’s moral justification. An adequate intersectional methodology, which begins from centering anti-oppression anger, reveals significant first-order lessons about the nature and normativity of anger. One is that we can make good sense of the normative relation of “basic desert,” even though anti-oppression anger is not retributive. Centering anti-oppression anger reveals further insights about our responses to anger, including important roles for agency and moral character in the ways we face and take up anger. There is, moreover, an ethics to being wrong that must be factored into our theories of emotions like anger.
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 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".