Social-ecological uncertainty and the (in)capacity to adapt: stakeholders’ perceptions post-red tide/salmon farming crisis in Chiloé Island (Chile)
Bibliographic record
Abstract
In 2016, a prominent social movement developed on Chiloé Island in protest against the consequences of the worst harmful algal blooms in Chile’s history. During the same period, with the national government’s authorization, the salmon farming industry dumped 9000 tonnes of dead fish into the sea less than 75 nautical miles off Chiloé. Research on environmental change in coastal zones shows that coastal communities suffer a broad array of stressors that challenge them socially, culturally, and economically. Climate change impacts, declines in marine species, and global market pressures, among others, create disturbances that increase local vulnerabilities. Yet limited attention has been paid to coastal communities exposed to large-scale industrial developments and the role of social conflict as a driver of change in converging social-environmental shocks, as the Chiloé crisis illustrates. Through a qualitative approach, this research describes the perceptions of key stakeholders regarding the multiple stressors of the red tide/salmon dumping crisis and the impacts of the crisis on their adaptive capacity. Results suggest that social adaptive capacity is challenged to its breaking point because of enduring environmental uncertainty, which in turn influences knowledge and perceptions about environmental changes, livelihood opportunities, and governance. Specifically, there are opposing narratives about the causes and consequences of algal blooms and marine degradation. Whereas the government attributes climate change to the issue, key players in the movement argue that the toxicity of industrial salmon farming is the primary cause. A few positive outcomes are associated with the social movement’s efforts, including a Supreme Court decision that favors the communities. However, national and local institutional responses have been short-sighted, and uncertainty undermines people’s ability to address future environmental challenges. Ultimately, we find that the social conflict and the dual attributions of the cause that followed the salmon mortality and algal bloom events are less about multiple stressors and, instead, are more fully about the decline in adaptive capacity that followed the social conflict itself.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".