The psychology of spite and the measurement of spitefulness.
Bibliographic record
Abstract
Spite is an understudied construct that has been virtually ignored within the personality, social, and clinical psychology literatures. This study introduces a self-report Spitefulness Scale to assess individual differences in spitefulness. The scale was initially tested on a large sample of 946 college students and cross-validated on a national sample of 297 adults. The scale was internally consistent in both samples. Factor analysis supported a 1-factor solution for the initial pool of 31 items. Item response theory analysis was used to identify the best performing of the original 31 items in the university sample and reduce the scale to 17 items. Tests of measurement invariance indicated that the items functioned similarly across both university and national samples, across both men and women, and across both ethnic majority and minority groups. Men reported higher levels of spitefulness than women, younger people were more spiteful than older people, and ethnic minority members reported higher levels of spitefulness than ethnic majority members. Across both samples, spitefulness was positively associated with aggression, psychopathy, Machiavellianism, narcissism, and guilt-free shame, and negatively correlated with self-esteem, guilt-proneness, agreeableness, and conscientiousness. Ideally, this Spitefulness Scale will be able to predict behavior in both laboratory settings (e.g., ultimatum games, aggression paradigms) and everyday life, contribute to the diagnosis of personality disorders and oppositional defiant disorder, and encourage further study of this neglected, often destructive, trait.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".