Indigenous knowledge as data for modern fishery management: a case study of Dungeness crab in Pacific Canada
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
ABSTRACT Introduction: Fisheries management is often data-limited, and conducted at spatial scales that are too large to address the needs of Indigenous peoples, whose cultures depend upon the local availability of marine resources. Outcomes: We combined Indigenous ecological knowledge with simulation modelling to inform modern fishery management. Semi-structured interviews with Indigenous fishers in coastal British Columbia, Canada, uncovered severe declines in the abundance and catches of Dungeness crab ( Cancer magister ) since the 1990s. We modelled the current probability of “successful“ crab harvesting trips—as defined by expectations from past catches by Indigenous fishers—using fishery-independent data from nine sites. These probabilities were very low (<20%) for all sites except one. Discussion: Our study highlights that local depletions, which Indigenous fishers attribute to commercial and recreational fisheries, have been widespread and undetected by federal managers who manage Dungeness crab at regional scales without fishery-independent data. Further, local depletions impacted the ability of Indigenous fishers to access traditional foods. Conclusion: Integrating Indigenous knowledge with scientific research is crucial to inform locally-relevant fisheries management and conservation.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it