Fishers' and scientists' social-ecological knowledge and Newfoundland's capelin fisheries
Bibliographic record
Abstract
Using a case study of the Newfoundland capelin fisheries, this thesis explores the potential benefits of a social-ecological approach to gathering fishers' knowledge, analyzing both fishers' and scientists' knowledge, and attempting to integrate insights from both knowledge forms. In doing so, the thesis employs data from two types of personal interviews with fishers, as well as findings from scientific studies, to highlight points of both agreement and disagreement between fishers and scientists on four major issues that were forefront in the Newfoundland capelin fishery in the 1990s. Using a social-ecological approach to knowledge, possible reasons are posed to understand why scientists and fishers disagree with each other and why some fishers disagree with others. -- The thesis demonstrates that this approach to understanding fishers' and scientists' knowledge is essential for projects that aim to critically assess and effectively integrate insights from these different sources. The thesis also sheds light on areas of scientific research that may require further research and analysis and proposes a series of policy recommendations that may strengthen future collaborative efforts that aim to integrate fishers' and fisheries scientists' knowledge.
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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 | 0.004 | 0.006 |
| 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 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".