Bibliographic record
Abstract
This paper explores a phenomenon that we will call “justified-but-misdirected anger”, in which one’s anger is grounded in or born from a genuine wrong or injustice, but is directed towards an inappropriate target. In particular, we argue that oppressive ideologies that maintain systems of gender, race, and class encourage such misdirection and are thereby self-perpetuating. We engage with two particular examples of such misdirection. The first includes poor white voters who embrace racist and xenophobic politics; they are justified in being angry about their own economic exploitation, but that anger is misdirected in a way that maintains capitalism (which is the appropriate target of their anger). The second includes so-called “incels” who embrace misogyny; they are justified in being angry about unrealistic and unhealthy standards of contemporary masculinity, but that anger is misdirected in a way that maintains patriarchy. One goal of exploring this type of justified-but-misdirected anger is to contribute to an anti-carceral feminism that helps to thread the needle between holding wrongdoers to account while also clearly identifying the oppressive ideologies that influence their actions.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 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".