How fisher‐influenced marine closed areas contribute to ecosystem‐based management: A review and performance indicator scorecard
Bibliographic record
Abstract
Abstract A rapid review of the literature on closed areas that recognize key ecosystem‐based management (EBM) principles of fisheries and biodiversity conservation and had fisher involvement was employed to review closed areas worldwide from a fisheries perspective and to develop a scorecard that can assess their efficacy. The review provided 523 abstracts of which 19 areas from various regions worldwide had peer‐reviewed studies that met strict selection criteria. Criteria included fisher involvement, biodiversity conservation and fisheries management objectives. A repeat search without “fisher” and synonyms found, 62,622 papers indicating that most closed area studies had no mention of any fisher involvement. The general success of the areas selected suggests that fisher involvement benefits both biological conservation and fisheries management. Fisheries and biodiversity conservation outcomes were not exclusive to any one type of management closure (e.g. MPA, Fishery Closure). Twenty‐four indicators were selected, designed to provide measurable targets. High scoring indicators included management, planning and socio‐economic indicators such as local support (100%), habitat protection (100%), conservation and fisheries objectives (100%), monitoring (91.7%) and fishers concerns (91.7%). Bio‐ecological‐based indicators scored lower in most cases for all types of areas. Fisheries closures rated as highly as the MPAs with respect to both fisheries and bio‐ecological indicators. The scorecard provided a reasonable means to evaluate management success in the light of often qualitative or missing data. Addressing the interests and utilizing knowledge of those affected by closures and familiar with the area, most often local fishers, is key to achieving management objectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.033 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".