Persistent chemicals, persistent activism: scientific opportunity structures and social movement organizing on contamination by per-and polyfluoroalkyl substances
Bibliographic record
Abstract
Engagement with science is a prominent feature for many social movements, yet the dimensions of that scientific engagement and bidirectional relationships between science and advocacy are incompletely theorized in social movement scholarship. While social movement scholarship has previously demonstrated the importance of external political and economic factors for social movement processes and efficacy, we show that the emergence and success of environmental health activism is also dependent on dynamic relationships between scientific evidence and lay demands for particular types of knowledge production and application. Despite decades of industrial production and widespread contamination, per- and polyfluoroalkyl substances (PFAS) were a politically obscure class of chemicals until a recent spike in attention from activist, regulatory, and scientific circles. Drawing from in-depth interviews with activists of PFAS-impacted communities, we develop the scientific opportunity concept to examine how activists create and mobilize scientific factors to support their goals, and how scientific factors, in turn, support the emergence of further activism. Dimensions of scientific opportunity include availability of funding streams, openness and receptivity of institutionalized scientific spaces, presence of collaborative or community-led research, methodological and technological advancements aligned with activist demands, availability of relevant scientific findings and datasets, and presence of prominent scientific allies. We conclude by discussing the relevance of our concept to a wide range of social movements addressing science and technology.
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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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".