Proximity under Threat: The Role of Physical Distance in Intergroup Relations
Bibliographic record
Abstract
Throughout human history, social groups have invested immense amounts of wealth and time to keep threatening out-groups at a distance. In the current research, we explored the relationship between intergroup threat, physical distance, and discrimination. Specifically, we examined how intergroup threat alters estimates of physical distance to out-groups and how physical proximity affects intergroup relations. Previous research has found that people judge threatening out-groups as physically close. In Studies 1 and 2, we examined ways to attenuate this bias. In Study 1 a secure (vs. permeable) US-Mexico border reduced the estimated proximity to Mexico City among Americans who felt threatened by Mexican immigration. In Study 2, intergroup apologies reduced estimates of physical proximity to a threatening cross-town rival university, but only among participants with cross-group friendships. In Study 3, New York Yankees fans who received an experimental induction of physical proximity to a threatening out-group (Boston Red Sox) had a stronger relationship between their collective identification with the New York Yankees and support for discriminatory policies toward members of the out-group (Red Sox fans) as well as how far they chose to sit from out-group members (Red Sox fans). Together, these studies suggest that intergroup threat alters judgment of physical properties, which has important implications for intergroup relations.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".