What’s hate got to do with it? Right-wing movements and the hate stereotype
Bibliographic record
Abstract
‘Hate stereotyping’ occurs when researchers foreground negative emotions, especially hate, as motivating right-wing social movements, epitomized by labels like ‘hate group’. This convention contradicts empirical evidence showing that hateful feelings and ideological prejudices are mostly insignificant for attracting and retaining members in such movements. Using contemporary theories of hate, this article demonstrates the concept’s limits and misuse in studying and theorizing the political Right. For instance, hate’s theoretical and methodological ambiguity sometimes leads scholars to confuse hatred with right-wing ideology and prejudice, which can obfuscate findings and spur dubious generalizations across political groups. Moreover, some researchers accept post-structuralist theories of hate as a substitute for vital data on emotions, motivations and meaning-making among right-wing actors. Hate explanations persist because they appeal to ‘common sense’ about intolerance, not because of their methodological integrity for studying right-wing movements. By foregrounding intolerance, hate stereotyping risks sustaining the dominant narrative that prejudices such as racism are deviant, and that racism is a problem of bad attitudes and fringe ideologies, rather than larger issues of systemic and structural inequality.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.025 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".