Inclusivity of Indigenous Knowledge Systems in Fisheries Management
Bibliographic record
Abstract
ABSTRACT Indigenous Peoples have developed knowledge systems that foster respectful and reciprocal relations between humans and other‐than‐human beings, supporting resilient ecosystems and societies. Despite the impacts of colonisation, Indigenous Knowledge Systems (IKS) endure in many parts of the world, and there is growing recognition that IKS can strongly improve fisheries management. During the last 5 years, Fisheries and Oceans Canada (DFO), the federal institution responsible for managing Canada's fisheries, released policies and strategies intended to make fisheries management more inclusive of IKS. To measure progress in their implementation, we applied 13 semiquantitative indicators and qualitative analyses of IKS inclusivity to a sample of 78 public documents produced or co‐produced by DFO to advise management decisions. Of these documents, ≈87% reported cases that did not meaningfully include Indigenous Peoples and their IKS, 9.0% reported cases in which Indigenous Peoples were included in some aspects of research but their IKS was not, ≈3% reported cases in which IKS contributed to objectives and elements of research design but the process privileged Western science over IKS, and only one document met a high standard for the pairing of IKS and Western science. The indicators that we developed in a Canadian context can be used, with locally appropriate revisions, to gauge the extent to which state governments in other countries are inclusive of IKS in fisheries management, thereby identifying shortcomings in law, policy, and practice and informing mitigation measures. Strengthening the inclusivity of IKS would enable more holistic approaches to fisheries management and benefit global conservation.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".