Enhancing fisheries education in Canada: The need for interdisciplinarity, collaboration, and inclusivity
Bibliographic record
Abstract
The Canadian Fisheries Research Network (CFRN) was initiated to increase interdisciplinary research capacity through enhanced, cross-sector collaboration. Training in the CFRN was important and unique for students because of the exposure to the realities of industry, government and academics working in close collaboration. As CFRN students, our goal for this paper was to assess whether Canada is appropriately preparing the next generation of fisheries graduates to tackle complex fisheries management problems. This assessment consisted of 1) a systematic review of fisheries-related education across Canada, 2) a reflection on our experience in the CFRN, and 3) comments on the importance of inclusive and interdisciplinary approaches in fisheries education and fisheries research. Based on our assessment, we concluded that the availability of fisheries education in Canada is limited, particularly with respect to interdisciplinary training. We contend that the CFRN enhanced our educational experience by fostering interdisciplinary and inclusive fisheries research. We recommend that the CFRN model be considered both in the development of fisheries education initiatives and in the design of future fisheries research.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".