Learning from an Interdisciplinary Research Experience Exploring the Impacts of Global Changes on Quebec’s Northern Shrimp Fishery
Bibliographic record
Abstract
Interdisciplinary research approaches are considered valuable in tackling complex challenges and developing richer understandings of how global changes will affect the future of social-ecological systems. Interdisciplinarity is increasingly used to bring together diverse forms of knowledge, take account of complexities, and identify pathways for adapting to global changes. Yet interdisciplinary research can be challenging for epistemological, methodological and operational reasons, and many researchers may lack guidance for developing effective processes, knowledge and skills. We share lessons from an interdisciplinary project conducted on the impacts of ocean changes on the Northern shrimp and its fishery in Quebec (Canada), from 2018 to 2021. These concern co-design, co-production of knowledge and co-dissemination of results by the research team. We present successes, shortcomings and suggestions focused on team building, network support and integration across disciplines in the context of research supporting fishery management, including marine biology, chemical oceanography, biogeography, genetics, sociology, and regional development.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".