Bibliographic record
Abstract
Environmental disasters are frequently catalysts for social and political change. Yet, disasters of similar scale and impact seem to encourage collective action in some cases but fail to do so in others. For example, while some large oil spills have generated mass nationwide (and international) protests, others have gone largely unnoticed and protests, if any, remained small and localized. If disasters are political triggering events, as the existing literature suggests, why do they often fail to generate large scale collective action? In fact, why do some highly damaging industrial environmental disasters succeed, and others fail to catalyze protest movements? This research strives to explain a variation in the occurrence and size of non-violent protest after industrial environmental disasters in advanced democracies. I examine the mobilizing effects of disaster type and location, the underlying societal conditions conducive to protest, and the ‘language of disasters’ in post-disaster communication. I argue that in addition to grievances, resources, political opportunities, and framing, uncertainty about disaster impacts plays a crucial role in the protest mobilization process, one that has not been fully explored by scholars. Specifically, while uncertainty may have a dampening effect on protest mobilization, this effect is conditioned by people’s pre-existing beliefs, and particularly political ideology. Left-leaning (i.e., more liberal) individuals resist the dampening effect of uncertainty, while right-leaning (i.e., more conservative) individuals embrace it. This research draws on theories of social movements and framing as well as insights from previously studied disasters; it involves an in-depth analysis of cases of industrial disasters with large environmental impacts, including the 2014 Mount Polley mine leak, the 2010 Deepwater Horizon oil spill, and the 2011 Fukushima nuclear disaster. The cases were selected due to the varying protest sizes following these events. To allow for a systematic examination of different factors linked to post-disaster protest, this research employs several methods and tools, including a geographic information systems (GIS) analysis, qualitative comparative analysis (QCA), content analysis, and survey experiment. Such multi-method approach is most suitable for answering the variety and complexity of questions this research poses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".