Precarious livelihoods: Examining the intersection of fish work and ecological change in coastal Jamaica
Bibliographic record
Abstract
Abstract Precarity has not been applied in the context of fisheries‐based work, even as working conditions in fisheries are emerging to be a real issue. There has been limited analysis of fish work outside the media spotlight or how changing ecological and biophysical conditions (e.g. climate change and its effects) intersect with poor working conditions. We use insights from fieldwork along the southwest coast of Jamaica, to ask the following questions: (a) what does precarity mean for mixed‐gear fishers working in the nearshore context, particularly with reference to working conditions and (b) how do changing ecological and biophysical conditions intersect with working conditions to further influence fisher precarity? Our results highlight how fishing livelihoods in Jamaica are generally precarious because of limited options for fish workers in this sector. Even so, certain fishing activities are far riskier than others – particularly for compressor dive fishers – and that levels of precarity are differentiated by age and fishing gear ownership. A more integrative (or social and ecological) approach to precarity analysis helps characterize and give nuance to fish work operating across time and space, and shows how such work intersects with ecological decline and further drives precarity. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".