Enhancing fisheries education and research through the Canadian Fisheries Research Network: A student perspective on interdisciplinarity, collaboration and inclusivity
Bibliographic record
Abstract
Fisheries involve complex problems not easily addressed by a single discipline, methodology, or set of stakeholders. In 2010, the Canadian Fisheries Research Network (CFRN) was initiated to increase fisheries research capacity in Canada through interdisciplinary and inclusive research collaborations. As post-graduate students in the network, we reflected on the type of training necessary to tackle fisheries problems and reviewed opportunities available at Canadian universities to receive such training. This paper presents an overview of fisheries education currently available in Canada, reflects on our training within the CFRN, and proposes improvements to fisheries education and research. Our review of the subject revealed few dedicated fisheries programs, limited interdisciplinary programs, few specialized fisheries training programs, and a heavy reliance on academic supervisors to secure research opportunities in fisheries. In contrast, the CFRN enhanced our training by deliberately focusing on tools and techniques to address fisheries issues, providing venues to foster interdisciplinary and inclusive research collaborations, and exposing the realities of stakeholder collaborations. We call for post-graduate-level fisheries education and research that is interdisciplinary, collaborative, and inclusive to produce well-rounded scientists and managers, and we suggest ways that universities, researchers, and funding agencies can incorporate these themes into fisheries education and 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".