Ignore fishers’ knowledge and miss the boat
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We describe five examples of how, by ignoring fishers’ ecological knowledge (FEK), marine researchers and resource managers may put fishery resources at risk, or unnecessarily compromise the welfare of resource users. Fishers can provide critical information on such things as interannual, seasonal, lunar, diel, tide‐related and habitat‐related differences in behaviour and abundance of target species, and on how these influence fishing strategies. Where long‐term data sets are unavailable, older fishers are also often the only source of information on historical changes in local marine stocks and in marine environmental conditions. FEK can thus help improve management of target stocks and rebuild marine ecosystems. It can play important roles in the siting of marine protected areas and in environmental impact assessment. The fact that studying FEK does not meet criteria for acceptable research advanced by some marine biologists highlights the inadequacy of those criteria.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it