When small acts are multiplied: Assessing everyday social justice behaviors
Bibliographic record
Abstract
Using the Act Frequency Approach, we drew on majority White, U.S. samples to create a new measure of social justice behavior and examine its correlates. Although existing measures of social justice behavior focus on engagement in collective action, participants in Study 1 (n = 137) were encouraged to nominate and evaluate a broad set of acts relevant to their daily lives. The final 17-item Everyday Social Justice Behavior (ESJB) scale reflects a range of global and domain-specific actions rated as prototypical by both 53 undergraduate novices and 20 social justice experts in Study 2. Participants in studies 3 (n = 388) and 4 (n = 613) were then asked to rate how frequently they perform the items. As expected, women and sexual minorities, and those with left political orientation, engaged in more everyday social justice behavior. Moreover, those reporting more everyday social justice behavior also scored higher in structural attributions of social change, intersectional awareness, ratings of the importance of and confidence in taking action, openness to experience, extraversion, and empathy, while being lower in social dominance orientation, system justification, and the need for cognitive closure. In addition, those high in ESJB also reported more progressive activist engagement and intentions. Relations with activism were modest, suggesting social justice activism and ESJB are somewhat distinct forms of social justice behavior. This measure should be of broader use in similar (majority White) samples; the measure development process can also be used to assess such behaviors in other samples and contexts.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".