Fluctuations in Prejudice Do Not Track Fluctuations in Ordinary Contact in Three 5-Wave “Shortitudinal” Studies Examining Daily, Weekly, or Monthly Intervals
Bibliographic record
Abstract
Intergroup contact is regarded as one of the most effective ways to reduce prejudice. However, recent longitudinal studies using contemporary statistical techniques (e.g., random intercept cross-lagged panel models [RI-CLPMs]) have failed to find evidence of within-person changes in prejudice following contact fluctuations. We propose that past time-lags may have been too long to capture change and conducted three studies with shorter time-lags of single days, weeks, or months. We also considered effects of positive versus negative contact frequency. We consistently found that people who are less prejudiced have more contact (i.e., between-person effects); however, fluctuations in naturally occurring contact were not followed by corresponding within-person changes in prejudice, suggesting shorter-term contact fluctuations are detached from prejudice. With abundant support for contact in the field, we argue that prejudice may be impacted by major contact events, or through gradually acquired cumulative experiences, but effects are not apparent when examining “thin-slices” of time.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".