Teaching and learning guide for: The role of individual differences in understanding and enhancing intergroup contact
Bibliographic record
Abstract
Intergroup contact, the direct or extended (or virtual/imagined) interaction with members of other groups, has enjoyed a long history in social psychology. Allport (1954) introduced the “Contact Hypothesis”, which has since evolved into a full and complex “Contact Theory” (Brown & Hewstone, 2005; see also Hodson & Hewstone, 2013; Pettigrew & Tropp, 2001; Turner, Hewstone, Voci, Vonofakou, & Christ, 2007). Across different types of groups, different types of contact, and different methodologies, researchers find that having more encounters with specific outgroup members tends to reduce prejudice toward that group as a whole (see meta-analyses by Davies, Tropp, Aron, Pettigrew, & Wright, 2014; Pettigrew & Tropp, 2006; Lemmer & Wagner, 2015). Importantly, contact works more reliably at reducing prejudice relative to other interventions (e.g., Beelmann & Heinemann, 2014). Yet researchers historically felt that individual differences in prejudice-proneness (e.g., authoritarianism) were either irrelevant to, or were obstacles to, contact-based prejudice reduction (see Hodson, Costello, & MacInnis, 2013). More recently, interest in individual differences in contact settings has grown steadily. This article serves as an education tool to not only teach students about intergroup contact and personality (among other individual differences), but to encourage them to consider the possibilities for learning and prejudice reduction when these two topics are conceptually integrated.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.120 | 0.057 |
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".