Decent work in fishing in a changing climate
Bibliographic record
Abstract
Climate change will increasingly impact the working conditions of employed fishers, who work in the most hazardous occupation in a sector already at high risk for forced labor and other decent work deficits. However, in comparison to other sectors, there has been little attention afforded to how climate change will impact working conditions onboard industrial vessels. Although the absence of a well-organized workforce makes it challenging to identify and anticipate climate impacts, this information is critical for designing effective strategies to mitigate them. In this paper we elucidate these emerging linkages in a conceptual framework that was developed through a review of the literature and a convening of government and academic researchers and worker representatives. Fishers are likely to be affected by direct climate hazards, such as injuries and illness from increased storminess and heat exposure, and indirect impacts, such as fatigue and poorer mental health outcomes from longer voyages and working hours as stock abundances change and shift because of warming waters. The power imbalances and denial of agency that create exploitative working conditions, including forced labor, will likely limit vulnerable fishers’ adaptive capacity, further entrenching inequities. The framework also highlights significant knowledge gaps that limit our understanding of fishers’ vulnerabilities and sector risks and that delay the development and implementation of evidence-based interventions. Without immediate and considered policy action informed by workers’ experiences, climate change will likely exacerbate and create new manifestations of decent work deficits in global fisheries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".