Enhancing fisheries education through the Canadian Fisheries Research Network: a student perspective on interdisciplinarity, collaboration and inclusivity
Bibliographic record
Abstract
Fisheries sciences and management involve complex problems not easily addressed by a single set of stakeholders or methodologies from one discipline; accordingly, the Canadian Fisheries Research Network (CFRN) was initiated to increase fisheries research capacity in Canada through interdisciplinary and inclusive research collaborations. We compared the value of the CFRN students’ learning experience to that offered in traditional fisheries programs at Canadian universities in training post-graduate students to tackle complex fisheries problems. This paper presents 1) a review of the current state of fisheries education across Canada and 2) reflections on our training within the CFRN, and challenges to implementing its innovative approach to fisheries education. We found few dedicated fisheries programs in Canada and concluded that fisheries research typically relies on securing a supervisor with an interest in fisheries. In contrast, the CFRN enhanced our university training through interdisciplinary and inclusive research collaborations, and by exposure to the realities of industry, government and academics collaborating for sustainable fisheries. We propose a new approach to post-graduate level fisheries education, one that combines interdisciplinarity, collaboration, and inclusivity to produce more capable fisheries scientists and managers. Furthermore, we made recommendations on how universities, researchers, and funding agencies can successfully incorporate these themes into fisheries education.
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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 | 0.010 | 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 | 0.014 | 0.008 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".