Understanding the limitations of current RFMO climate change adaptation strategies: the case of the IATTC and the Eastern Pacific Ocean
Bibliographic record
Abstract
While Regional Fisheries Management Organizations (RFMOs) face many challenges in their pursuit of sustainable resource development, climate change is among the most pressing and least addressed. Research has identified a host of expected or ongoing physical, biological, ecological, and social impacts of climate change on the marine environment, creating a strong climate change adaptation imperative for RFMOs. Through a case study of the Inter-American Tropical Tuna Commission (IATTC), we describe two serious limitations of current RFMO climate change adaptation strategies: (1) a weakened efficacy of resource management and conservation policies caused by viewing climate change as a general climate stressor rather than a unique environmental challenge, and (2) a reliance on incremental policy reform, problematic because it may not enable a pace or scale of policy change proportional to the sustainable development challenges created by a rapidly changing ocean. We discuss the benefits and drawbacks of incrementalism and outline potential solutions to the environmental and structural challenges facing the IATTC and other RFMOs, including the concept of adaptation pathways.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| 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".