Social Categorization Improves Intergroup Helping: A Behavioral Field Experiment
Bibliographic record
Abstract
Historically, the intergroup relations literature has concluded that social categorization leads to intergroup conflict, but recent research (Leonardelli & Toh, 2011) argues that it can lead to greater intergroup helping because it differentiates a group in need from one that can give aid. In this field experiment, we test this prediction for the first time causally and behaviorally, in the domain of helping behaviour between strangers, and explore the predicted explanatory mechanism (perceptions of relative resource; i.e., members of one group have resources that would benefit those of another group). Trained confederates dressed as students (e.g., backpack, casual clothing) or as business professionals (e.g., suit, tie) on a university campus were instructed to appear lost. Consistent with the relative resource explanation, student passerby were more likely to spontaneously assist confederates whom they saw as business professionals than as fellow students because they believed the business professionals knew less than they did about on-campus locations. This research has implications for intergroup power relations, knowledge sharing, and diversity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".