Bibliographic record
Abstract
Reducing prejudice has long been a central pillar of efforts to improve intergroup relations. Intergroup contact (i.e., fostering positive interactions between members of different groups) is arguably the most reliably effective tool for prejudice reduction known to social scientists. Yet, major challenges inherent to studying and applying intergroup contact hold it back from readiness for practical application at scale and in the real world. Because it is so widely used and allows for low- or no-cost, scalable intervention, I propose that using social media to study intergroup contact may help us push contact research forward to applied readiness. I therefore present a set of studies developing, validating, and testing an approach to using intergroup contact in the context of social media to intervene to reduce prejudice. In Study 1, I find that intergroup contact on social media is associated with less prejudice and more positive intergroup outcomes in a similar way as intergroup contact that takes place in-person. In Study 2, I find that the associations can be observed longitudinally and using behavioral variables collected in the field (i.e., on participants’ real Twitter accounts). In the Study 3 Pilot, I find that manipulating the racial demographics of accounts posting to participants’ real Twitter feeds impacts how much intergroup contact they subjectively experience on Twitter. Finally, in Study 3, I find that manipulating the racial demographics of accounts posting to participants’ real Twitter feeds may have a causal effect on their racial prejudice, but only when participants were interested in the content that was posted. These results suggest that a no-cost, scalable intervention strategy for reducing prejudice and improving intergroup relations through intergroup contact on social media may be effective. They also speak to the need for interventionists to attend to participants’ interests and motivations, particularly in field settings. Open materials and data for all studies and pre-registrations for Studies 2 and 3 can be found on the Open Science Framework: https://osf.io/jz8hm?view_only=64ac338f0a49482194df29149157180e.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".