Activists’ Strategic Interactions, Collaborative Tactics, and Local Environmental Performance
Bibliographic record
Abstract
This paper uses social movement theory and strategic interactions perspective to examine social movement organizations’ use of a diverse set of tactics in their interactions with local communities, firms, and facilities. Using unique, geocoded data on 527 environmental movement organizations’ (EMOs) reported interactions and toxic chemical-based environmental indicator data between 2000 and 2015, we examine the effect of EMOs’ strategic activities and collaborative interactions on local environmental performance and the conditions under which these effects are amplified. Extending and complementing existing measures of movement activities, we suggest a new measure using local strategic activities that better captures local activists’ influence on the local environment. Our empirical analysis also demonstrates that EMOs’ collaborative interactions significantly improve local facilities’ environmental performance and that the effect of EMOs’ collaborative interactions is stronger in communities with higher level of disruptive protests in previous years, lending support to the radical flank effect hypothesis. Our findings contribute to social movement theory and organizational theory by demonstrating the material impact of movement organizations’ strategic interactions on local corporate facilities’ environmental actions and by highlighting the manner in which the level of local EMOs’ past contentious engagement may moderate the effects of cooperative engagement.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| 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".