What’s hate got to do with it? Right-wing movements and the hate stereotype
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it