The psychology of hate: Moral concerns differentiate hate from dislike
Bibliographic record
Abstract
We investigated whether any differences in the psychological conceptualization of hate and dislike were simply a matter of degree of negativity (i.e., hate falls on the end of the continuum of dislike) or also morality (i.e., hate is imbued with distinct moral components that distinguish it from dislike). In three lab studies in Canada and the US, participants reported disliked and hated attitude objects and rated each on dimensions including valence, attitude strength, morality, and emotional content. Quantitative and qualitative measures revealed that hated attitude objects were more negative than disliked attitude objects and associated with moral beliefs and emotions, even after adjusting for differences in negativity. In study four, we analyzed the rhetoric on real hate sites and complaint forums and found that the language used on prominent hate websites contained more words related to morality, but not negativity, relative to complaint forums.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".