Bibliographic record
Abstract
The analysis of anger’s role in politics needs to overcome the oversimplified views of anger as positive or negative. Instead, a more useful normative question is one that asks how we should distinguish between forms of anger that are appropriate or properly political, and those that are dangerous and impermissible, or antipolitical. By drawing on a conflict-theory framework that makes a normative distinction between agonistic and antagonistic conflicts, I argue that properly political anger should meet two criteria: fittingness and boundedness. Anger can sometimes be a fitting response to our unjust world. However, fittingness is not enough; it also matters how anger is channeled or acted upon in the public realm. Moreover, this paper contends that anger at structural injustice may relatively easily go wrong, as mistargeted or boundless anger. In light of these challenges, I argue that introducing a goal-frustration type of anger (as distinct from blaming anger) can prevent construing anger too narrowly, as always involving moral blame, and that one can learn (to some extent) to regulate one’s anger. At the same time, I acknowledge that the uptake that anger receives plays an essential role in creating an environment that favors (or disfavors) that anger at structural injustice remains bounded.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".