Knowledge Gaps and Management Priorities for Recreational Fisheries in the Developing World
Bibliographic record
Abstract
Millions of individuals worldwide rely on recreational fishing activities for leisure, food, and employment. Recreational fishing is the dominant freshwater fisheries sector in much of the highly developed world and plays a growing role in the marine realm, but in developing countries recreational fisheries occur within a different set of contextual conditions. Little is currently known about attributes of the recreational fishing sector in many developing countries. A survey of fisheries experts designed to identify knowledge gaps surrounding recreational fishery development was conducted to gather information on fishery attributes in developing countries. These surveys were supplemented with a review of relevant literature. Results show that recreational fishing is socially important and is expected to grow in most countries surveyed. Recreational fisheries were described as mainly consumption oriented in these regions. Most often, nonresident tourists used marine waters and resident recreational fishers used fresh waters. There was strong agreement among respondents on the need to address data deficiencies. The knowledge gaps and management needs identified can support international bodies and recreational fishing organizations (such as the regional fisheries bodies of the Food and Agricultural Organization of the United Nations, and local and international fishing associations) to support sustainable development and management of the global recreational fisheries sector.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".