Intergroup contact research in the 21st century: Lessons learned and forward progress if we remain open
Bibliographic record
Abstract
Abstract This article presents the 2021 JSI special issue on intergroup contact, which we designed to offer a fresh outlook on a rapidly expanding literature on the antecedents, dynamics, and consequences of interactions between members of opposing groups in society—or intergroup contact. We start by discussing the results of a bibliographic search of intergroup contact research between 1937 and 2021 and organizing our analysis around two distinct phases of this research, as they are demarcated in volume and quality by Pettigrew and Tropp's landmark meta‐analysis in 2006. We then turn our attention to an overview of the 12 review and commentary articles contributing to the special issue, which reflect advancements in themes, methodologies, and analytics of the last 15 years of research. We argue that this second generation of research has effectively addressed influential and legitimate critiques of the literature and, as a result, led to a more complex and nuanced understanding of intergroup contact that can now be readily harnessed by social cohesion practitioners and policy makers to increase the efficacy of contact‐based interventions in society. We conclude by calling on a third generation of research on intergroup contact that fully harnesses diversity of ideas, peoples, and minds and keeps in close check unproductive dynamics that stifle scientific progress, and pose a threat to healthy and safe research communities. Together with the 50 diverse contributors of this special issue, we commit to making the intergroup contact research community, like the topic of intergroup contact itself, diverse and inclusive.
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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 | 0.098 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.023 | 0.042 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".