How much time and who will do it? Organizing the toolbox of climate adaptations for small-scale fisheries
Bibliographic record
Abstract
Adaptation to climate impacts will be necessary for small-scale fisheries and fishers (SSFs) to safeguard their food security, livelihoods, and cultural heritage. SSFs are often vulnerable to environmental impacts due to the place-based, multi-scale and direct dependencies on local ecosystems, and generally fewer resources or abilities for relocation, diversification, and modification of their fishing practices. Strategic adaptation is therefore essential. This study emphasizes the timelines, requirements, and burdens of implementing existing and proposed adaptations, e.g., who pays, who does the work, and how long would it take? To categorize possible actions (tools) for analysis, we adapt the FAO climate adaptation framework and propose five areas of action: Institutional, Communication, Livelihood, Risk Resilience, and Science. Our results highlight two interconnected trends; first, the burdens and benefits of proposed climate adaptations are unevenly distributed, usually against fishers themselves. Second, there is a general lack of research focusing on the equity implications of current governance structures that de-emphasize fisher’s needs. This creates a lack of understanding among policy makers about the adaptation priorities of SSFs, and what resources or support they would need to implement them. We applied this framework to a case study involving octopus SSFs in Yucatán, Mexico. Interview results reinforce the finding that adaptation strategies that fishers thought would be most important for them (e.g. changes in policies/regulations to improve healthcare, reduce excess capacity, or reinforce fishing laws) were actions they could not often realize without external support; conversely, tools often proposed as “easier” by non-fishers (e.g. changing jobs, fishing gears, or going further out to sea) were not seen as particularly viable to fishers. Due to these mismatches, we argue there is a need to go beyond the classical focus on quantifying climate vulnerability towards a stronger emphasis on prioritizing adaptation strategies to meet the goals of fisherfolk themselves and aligning organizational and governance structures accordingly. The toolbox organization framework we propose can serve as an initial guidance for many fishing communities, decision makers and other stakeholders to anticipate implementation needs and find the right tools to adapt to future climatic conditions and prevent negative socioeconomic and ecological impacts.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".