Determinants of small-scale fisheries’ transformative responses under increasing climate change impacts in Nayarit, Mexico
Bibliographic record
Abstract
Progressive climate-driven environmental changes are and will increasingly be threatening the livelihoods and food security of coastal communities worldwide. This study, conducted in the climate change hotspot of Nayarit, Mexico, analyzes data collected through face-to-face interviews with 437 small-scale commercial fishers. We examine the factors influencing fishers’ transformative behavior, focusing on two main responses: changing the main livelihood and completely exiting the fisheries; each assessed on two hypothetical scenarios of 50% and 75% sustained catch decrease, directly coupled with the respective economic loss. Under a 50% catch decrease scenario, 35% decided to look for a new main livelihood while 15% considered exiting small-scale fisheries (SSF). These percentages increased under the 75% scenario, with 52% opting to seek a new main livelihood and 32% contemplating exiting. Through a mixed effects survival Cox model, our findings reveal that the social organization of the system, driven by the uneven access to permits, strongly affects fishers’ decision to adopt transformative responses. In such situations, fishing cooperatives and patron-client relationships facilitated transitions into a new main livelihood and exiting the fishery under large impact scenarios. These results highlight the importance of social capital and how the management systems in place can impact fishers’ resilience to climate change. Our novel study illustrates the usefulness of survival analysis in climate change adaptation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".