Bystanders, protesters, journalists: A qualitative examination of different stakeholders’ motivations to participate in collective action
Bibliographic record
Abstract
Both bystanders and journalists can play important roles in mobilizing and supporting social movements. However, there are few empirical studies examining and contrasting their violent and nonviolent collective-action motivations or perspectives on social movement goals. This study presents a comparative analysis of motivations to engage or stand aside from social unrest comparing bystanders ( n = 9) and journalists ( n = 7) motivations against those of protesters ( n = 35). Thematic qualitative analysis of interview data using a Social Identity Model of Collective Action framework examined differences in motivations and goals across each group, as well as the influence of violent protest repertoires on participation behaviors. Identified barriers to participation include bystanders’ lack of issue consensus, low efficacy perceptions, and negative views of violent action. Our results also lend support to the predictive validity of collective identification, anger, and injustice in motivating participation in collective action. Journalists’ collective identity precluded overt protest participation. However, their emotional responses to injustice or violent actions generated tensions between their role obligations and desire to intervene. Implications for future research on collective-action responses to injustice 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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".