Extracting implicit theories about the risk of coercive control in romantic relationships
Bibliographic record
Abstract
Abstract People readily make attributions about the likely behavior of others, based on very limited information. We exploited this tendency to assess people's sensitivity to personological and social‐circumstantial evidence of risk of coercive control in romantic relationships, by unobtrusively varying information about a fictitious couple in a between‐groups design and asking viewers to make predictions about the feelings and behavior of the three characters–a man, his girlfriend, and his sister. Key features of the story were systematically altered to elicit attributions of the man's aggressive and jealous inclinations to see if people are sensitive to the psychological link between sexually proprietary inclinations and risk of violence. The story manipulations were effective in eliciting attributions of the man's aggressive inclinations, of the woman's polyandrous inclinations, and of the man's likely jealousy. As expected, people predicted that an aggressive and jealous man would be likely to use violence and other controlling actions against his girlfriend.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".