How are Today’s Workers Mobilizing to Address Social and Environmental Challenges?
Bibliographic record
Abstract
This study takes the context of bottom-up activism and explores the ways that today’s workers are mobilizing to address societal challenges. It comprises a qualitative comparative research design using process study to identify the practices through which internal activists attempt to influence their firms. Drawing from social movement literature, the work explores recent employee activists’ campaigns’ use of tactics, informational content in their claims, and tools to drive two organizations to address societal challenges. We identify two new insider activism tactics: shareholder resolutions and resignations. Findings also suggest that tactics change over time. Facing unmet demands, activists shift from institutionalized mechanisms of change to confrontational and disruptive tactics. Informational content in their confrontational claims reveals that workers expose internal knowledge about their firm’s market and non-market activities. We also identify two demand escalation strategies: horizontal and vertical, that can explain outcome variations. Theoretically, this suggests that employee activists today have more complex repertoires than initially thought. The deployment of these repertoires changes over time as activists are met with resistance from the firm. We also find that organizations may offer concessions but still sanction employees, suggesting a more nuanced view of organizational response to internal activism.
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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 | 0.003 | 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 | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".