Contribution of marine fisheries to worldwide employment
Bibliographic record
Abstract
Abstract Marine fisheries contribute to the global economy, from the catching of fish through to the provision of support services for the fishing industry. General lack of data and uncertainty about the level of employment in marine fisheries can lead to underestimation of fishing effort and hence over‐exploited fisheries, or result in inaccurate projections of economic and societal costs and benefits. To address this gap, a database of marine fisheries employment for 144 coastal nations was compiled. Gaps in employment data that emerged were filled using a Monte Carlo approach to estimate the number of direct and indirect fisheries jobs. We focused on estimating jobs in the small‐scale fishing sector. We characterized small‐scale fishing as (i) primarily geared towards household consumption or sale at the local level; (ii) conducted at a low level of economic activity; (iii) minimally mechanized; (iv) conducted within inshore areas; (v) minimally managed; and/or (vi) undertaken for cultural or ceremonial purposes. In total, we estimated that 260 ± 6 million people are involved in global marine fisheries, encompassing full‐time and part‐time jobs in the direct and indirect sectors, with 22 ± 0.45 million of those being small‐scale fishers. This is equivalent to 203 ± 34 million full‐time equivalent jobs. Study results can be used to improve management decision making and highlight the need to improve monitoring and reporting of the number of people employed in marine fisheries globally.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".