Perceptions of Structural Injustice and Efficacy: Participation in Low/Moderate/High‐Cost Forms of Collective Action
Bibliographic record
Abstract
Prior studies on perceptions of structural disadvantage and injustice, efficacy, and collective action have suffered from two major limitations: (1) they have used single‐country samples, usually of economically advanced countries, and (2) generally theorized and investigated perceptions of structural injustice and efficacy separately. Drawing on value‐expectancy theory, we provide an integrated theory to predict direct and conditional effects of efficacy and perceptions of structural disadvantage and injustice on collective action within countries. To address the limitations of previous research, we use cross‐national data of 29 countries, including economically advanced and less advanced nations, to test how well these hypotheses explain within‐country variation in collective action. We find that internal efficacy is significantly and positively associated with low‐ and moderate‐cost collective action, whereas organizational embeddedness, a proxy for political efficacy, is significantly and positively associated with low‐, moderate‐, and high‐cost collective action. Perceptions of legitimate and unjust structural disadvantage are also positively associated with all types of collective action. Importantly, the positive effects of both types of efficacy on high‐cost collective action are conditional on perceptions of structural injustice. That is, participation in high‐cost collective action is more likely for those who are both efficacious and perceive structural disadvantage as unjust.
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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 | 0.004 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".