How feeling connected to one’s own community can increase support for addressing injustice impacting outgroup communities
Bibliographic record
Abstract
How can agents of social change increase public support for minority communities? In three studies, we demonstrate how heightened feelings of community connection can predict support for addressing injustice in minority communities. Community connection, when experimentally evoked (Study 1) or measured (Study 3), was associated with heightened support for the government addressing substandard conditions in an African American housing project (Studies 1 and 3) and Native American reservations (Study 1). Mediation analyses revealed that this effect emerges, at least in part, because of a heightened perceived value of all communities—not merely one’s own (Studies 1 and 3). One reason that stronger feelings of community connection lead to (Study 2) or are associated with (Study 3) greater valuing of communities is a strengthened superordinate community identity. We tested additional potential mediators of the community connection–support relationship; out-group identification mediated but outgroup attachment did not. Implications for social change are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".