Bibliographic record
Abstract
States of Rage permeate our culture and our daily lives. From the anti-Catholic protests of ACT-UP to the political posturing of Al Sharpton, from the LA Riots to anti-abortion gunmen murdering clinic personnel, the unleashing of rage, marginalized or institutional, has translated into dead bodies on our campuses and city streets, in our public buildings and in our homes. Rage seems to have gained a currency in the past decade which it previously did not possess. Suddenly we appear willing to employ it more often to describe our own or others' mental states or actions. Rage succinctly describes an ongoing emotional state for many residents and citizens of the United States and elsewhere. States of Rage gathers for the first time a critical mass of writing about rage--its function, expression, and utilities. It examines rage as a cultural phenomenon, delineating its use and explaining why this emotional state increasingly intrudes into our social, artistic, and academic existences. What is the relationship between rage and power(lessness)? How does rage relate to personal or social injustice? Can we ritualize rage or is it always spontaneous? Finally, what provokes rage and what is provocative about it? Essays shed light on the psychological and social origins of rage, its relationship to the self, its connection to culture, and its possible triggers. The volume includes chapters on violence in the workplace, the Montreal massacre, female murderers, the rage of African- American filmmakers, rage as a reaction to persecution, the rage of AIDS activists, class rage, and rage in the academy.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".