Examining Anger’s Immobilizing Effect on Institutional Insiders’ Action Intentions in Social Movements
Bibliographic record
Abstract
We theorize that anger incited by a social movement, which has a mobilizing effect among outsider activists, might immobilize collective action intentions for institutional insiders—those sympathetic to the movement and employed by its target. We conducted initial field surveys across a spectrum of social movements, including Occupy Wall Street and #metoo, as well as those related to business sustainability and gun control, which showed that institutional insiders are often just as angry as outsider activists. But the evidence from those surveys did not show that social movement anger translated into collective action intentions among institutional insiders. We tested our theory deductively with an experiment conducted with participants who were supportive of social movement issues in their organizations. Overall, our results show that anger about a social movement issue relates to greater collective action intentions among outsider activists but not among institutional insiders. Instead of anger emboldening institutional insiders to act despite the potential costs, anger triggers fear about the potential negative consequences of collective action in the workplace, which in turn results in withdrawal. While social movements often rely on anger frames to mobilize sympathizers, our work suggests that this practice may paradoxically cause fear that immobilizes those uniquely positioned to be able to influence organizations to change.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".