Intergroup contact, outgroup knowledge and advantaged group collective action: can who you know and what you know promote social change?
Bibliographic record
Abstract
Some groups in society unjustly hold greater social, economic, and political power over others, placing some groups in more advantaged and others in more disadvantaged positions. One way to challenge group-based inequality and promote social change is through collective action (e.g., protests, petitions, advocating). Most often, disadvantaged group members engage in collective action. However, when advantaged group members engage in solidarity-based collective action, it can heighten a movement's momentum. Four motivators of collective action among advantaged (and disadvantaged) group members have been identified: identification with the cause, anger about injustice, morality, and group efficacy. We examined what precedes these motivations regarding White Canadians' collective action benefitting Indigenous communities and White Americans' collective action benefitting Black communities. We examined two potential antecedents of advantaged group collective action motivation, intergroup contact and knowledge about the outgroup. In both samples, intergroup contact and knowledge of the outgroup were consistently indirectly associated with collective action through identification with the cause as well as through identification with the cause and anger about injustice. Of the multiple forms of intergroup contact and knowledge examined, the strongest associations were observed for higher quality contact and knowledge of systemic racism. These results have implications for both theory and intervention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".