Seafarers in fishing: A year into the COVID-19 pandemic
Bibliographic record
Abstract
This paper builds on our earlier publication that examined COVID-19, instability and migrant fish workers in Asia during the initial six months of the pandemic. Drawing on interviews with port-based support organizations and various other international organizations, we outline how pre-existing structural marginalizations of seafarers in distant water fishing has made them particularly vulnerable to the negative impacts of pandemic management policies for seafarers. We focus our analysis on obstacles to crew change and reduced access to crucial shore services. The basis of these longer term marginalizations includes the exclusion of fishing from the Maritime Labor Convention, the marginal status of fishing among global organizations concerned with seafarers, the dispersed ownership of fishing vessels compared to concentrated corporate ownership in shipping, lack of unionization, and frequent inaccessibility of consular assistance in fishing ports. We also highlight differences among important fishing ports, showing that repatriation of crew and access to shore services is the outcome of negotiation among a constellation of port-based actors.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".