Can (elaborated) imagined contact interventions reduce prejudice among those higher in intergroup disgust sensitivity (<scp>ITG‐DS</scp>)?
Bibliographic record
Abstract
Abstract Intergroup disgust sensitivity (ITG‐DS) reflects an affect‐laden revulsion toward out‐groups. Previous attempts to weaken its prediction of prejudice have failed. Given that clinical approaches to disgust sensitivity successfully utilize mental imagery, we consider contact simulation interventions. Participants were randomly assigned to control, standard imagined contact, or an elaborated contact condition (elaborated imagined contact [EIC]; detailed imagination involving physical contact with a homeless person, with relaxation instructions). Both contact conditions (vs. control) significantly weakened the link between ITG‐DS and prejudice, yet only EIC weakened the relation between ITG‐DS and out‐group trust. Mediated moderation analysis confirmed that EIC significantly attenuated the link between ITG‐DS and prejudice through increasing trust. Clinically relevant treatments are thus valuable in severing the association between (a) ITG‐DS and (b) lower out‐group trust and greater out‐group prejudice.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".