More than fish : political knowledge in the commercial fisheries of British Columbia
Bibliographic record
Abstract
Through ethnographic research based primarily in Prince Rupert, British Columbia, this dissertation explores the ecological and social significance of commercial fishers' political knowledge. Moving beyond the ecological thrust of local knowledge research, this ethnography emphasizes the material and political basis of fishers' perceptions and understandings. The study focuses on the politicized experience of commercial fishers in resource management contexts and the way in which their knowledge is constructed and positioned by power relations, resource competition, and economics. Analyzing a series of local knowledge encounters in the fishing industry of British Columbia - moments of conflict between competing knowledges over issues of conservation, co-management, and research - this dissertation reveals the significance of fishers' political knowledge in determining their fishing behaviour and their reactions to fisheries policy and to fisheries research. Fisheries regulation is explored as the defining force impacting livelihoods of fishers and shaping their knowledge, by structuring fishers' interaction with the environment, and with each other. This dissertation historicizes, problematizes, and differentiates local knowledge, emphasizing the entanglement of ecological and political knowledge as forms of knowledge that are implicated in the construction of each other. This dissertation argues for a more holistic approach to fisheries knowledge research, which does not focus only upon the ecological knowledge of resource users. Rather than only asking fishers what they know about fish, researchers must ask them about fisheries in order to explore and resolve the structural problems of human resource use.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".