Page 28, Evaluations and Policy Explorations Small Versus Large-Scale Fishing Operations In The North Atlantic
Bibliographic record
Abstract
Abstract: This paper compares small and large-scale fishing operations in the North Atlantic, by examining key policy relevant variables such as (i) the number of fishers they employ, (ii) the proportion of total annual catch that is landed by the two groups, (iii) the value of the catch they land, and (iv) annual catch that goes to the reduction industry relative to its use for direct hu-man consumption. We gathered data from the litera-ture to analyze the performance of the two sectors for the Canadian and Norwegian fishing fleets. We then used these country case studies to make inferences on how these two sectors perform at the level of the North Atlantic. Results from the analysis indicate, among other things that, small-scale fisheries employ more people for the same landed value, and that more of their catch is used for direct human consumption than large-scale fisheries. In some countries large-scale op-erations were more profitable (e.g., Norway) but there were countries in which small-scale operations did bet-ter (e.g., France). All in all, this study indicates that small-scale fisheries are better positioned to meet sev-eral of the policy goals set by both national govern-ments and international organizations on the use of ocean resources.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".