Balanced harvesting in fisheries: economic considerations
Bibliographic record
Abstract
Abstract This paper explores economic aspects of a recent proposal to shift fisheries to a “Balanced Harvesting” (BH) strategy, as a means to achieve the goal, set by the Convention on Biological Diversity and related to the Ecosystem Approach to Fisheries, of “conservation of ecosystem structure and functioning” within fishery ecosystems. Studies indicate that a BH strategy—broadening the range of species and sizes caught in the aquatic ecosystem, and lowering exploitation rates for some conventionally targeted species—may provide improved ecological performance relative to conventional harvesting strategies. However, the potential economic implications have received little attention to date. This paper provides a preliminary economic assessment of BH, focusing on six main themes: (i) assessing benefits and costs, (ii) factors affecting the economics of BH, (iii) economic issues in implementing the ingredients of BH, (iv) effects of incremental and/or partial implementation of BH, (v) transition options within the harvesting sector of the fishery, and (vi) distributional impacts arising across fisheries, fleet sectors, and fishing gears, and between the present and the future.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".