Integrating Local Ecological Knowledge into Fisheries Planning and Management: Another Context for Knowledge Management Research?
Bibliographic record
Abstract
An integrative review of 121 recent studies on collecting and utilising local ecological knowledge (LEK) for fisheries science and management suggests that insufficient attention is given either to conceptualising LEK or to the critical issue of how LEK is defined. Elements from knowledge management guided the analysis.Une revue intégrant 121 études récentes traitant de la collecte et l’utilisation du savoir écologique local pour la science et la gestion du domaine de la pêche suggère que ce domaine est négligé, particulièrement en ce qui concerne la conceptualisation du savoir écologique local ou encore la question fondamentale à savoir comment ce savoir est défini. Des éléments de la gestion des connaissances ont guidé cette analyse.
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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 | 0.038 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.027 | 0.060 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".