Strengthening community‐based fisheries monitoring programs with Indigenous perspectives
Bibliographic record
Abstract
Community-based monitoring (CBM) programs are increasingly recognized as essential for adaptive environmental stewardship. Yet, the CBM literature often highlights successful cases and privileges evaluations by external experts over those of community members themselves. To address this gap, we drew on insights from 23 semistructured interviews with Cree fishers, community members, and program administrators of the James Bay Cree Nation of Wemindji (Eeyou Istchee, northern Québec). The respondents participated in a 22-year subsistence fishing monitoring program. Interviews explored participants' experiences with the program and their interpretations of interannual variations in fishing activity based on the monitoring data. Although a general decline in annual fish catches was observed, data accuracy and utility were constrained by inconsistencies in monitoring protocols. Respondents identified several opportunities for improvement, including expanding fishers' roles beyond data collection; incorporating Cree knowledge, particularly women's knowledge, in program design; and ensuring the timely and accessible communication of results. Our findings showed that CBM initiatives grounded in full Indigenous participation at all stages-from design to data interpretation and use-can enhance both program outcomes and self-determined environmental stewardship. To support similar efforts elsewhere, we codeveloped an evaluation rubric outlining key criteria for assessing and strengthening current and future Indigenous CBM programs.
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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 | 0.054 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.001 | 0.002 |
| 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 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".