Towards an improved understanding of community-based monitoring: \nA case study of the Wemindji Community Fisheries Program
Bibliographic record
Abstract
Community-based monitoring (CBM) is widely recognised as a cost-effective alternative to conventional externally-driven, professionally executed monitoring. It has the potential to improve understanding of wildlife and ecosystems, enhance local authority and capacity, and contribute to the inter-generational transmission and cross-cultural exchange of knowledge. CBM can take a variety of governance approaches, including three categories of CBM involving indigenous communities: contributory monitoring (limited to local inputs); collaborative monitoring (roughly equal partnerships); and community-led (local control over all aspects). Unfortunately, few assessments of local indigenous perspectives are available within the field of CBM. This thesis addresses this gap by drawing upon the experience of a James Bay Cree First Nations community with one of the longest running subsistence fisheries monitoring programs ever conducted in the Canadian north. Specifically, we identify the benefits and challenges experienced as a result of twenty-three years of the Wemindji Coastal Fisheries Monitoring Program. The study uses semi-structured interviews and participant-based observations to facilitate the identification of program components, with a strong emphasis on the perspectives of local Cree program participants and administrators. It is hoped that my findings can contribute to the design and implementation of locally meaningful, and culturally appropriate, CBM programs that simultaneously maximize knowledge and labour inputs from local indigenous resource users.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".