A Participatory Approach to Identifying Research Needs for Community-Based Fishery Management
Bibliographic record
Abstract
Abstract.—This paper reports on a project to engage researchers and fishers together in adapting social science approaches to the purposes and the constraints of community-based fisher organizations. The work was carried out in several locations across Canada’s Maritime Provinces, with an underlying rationale based on three major arguments. First, effective community-based management requires that managers are able to pose and address social science questions. Second, participatory research involving true cooperation at all stages can support this process. Third, there is a need to overcome practical and methodological barriers faced in developing participatory research protocols to serve the needs of community-based management while not demanding excessive transaction costs. This paper reports on work with fisher organizations, both aboriginal and nonaboriginal, in identifying social science priorities and undertaking small-scale research projects to meet these needs. Several research themes proved crucial, notably, power sharing, defining boundaries of a community-based group, access and equity, designing effective management plans, enforcement, and scaling up for effective regional and ecosystem-wide management. The research results demonstrate the effectiveness of extending participatory methods to challenge traditional scientific notions of the research process. 588 WIBER ET AL.
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.
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 | 0.178 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.028 | 0.021 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.026 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".