Institutionalizing co‐production diplomacy in contexts of long‐term epistemological conflict: A case study of cod fisheries governance
Bibliographic record
Abstract
Abstract Knowledge co‐production is a collaborative approach to research that seeks to enable transformative societal change and improve outcomes in natural resource management and sustainable development. Instituting knowledge co‐production requires that researchers, decision‐makers, and stakeholders be willing to work together towards shared goals. In the context of fisheries management, co‐production represents a significant departure from the technocratic discourses and governance practices that have characterized decision‐making for decades. Moreover, some fisheries contexts have been plagued by persistent and seemingly intractable epistemological conflicts between stakeholders and decision‐makers. Such situations complicate the implementation of co‐production and raise questions about the extent to which researchers can achieve the aims of co‐production in situations of distrust, amenity, and entrenched positions. We use the case study of Northern Cod, a stock of Atlantic Cod (Gadus morhua) governance in Newfoundland and Labrador, Canada, a case of long‐standing conflict between the regulator, fishers, Indigenous peoples, and industry parties, to explore whether and how co‐production can enable collaborative research leading to “transformative societal change.” We find five factors complicating uptake of co‐production in the governance of Northern Cod: (i) competing perspectives exist regarding the relative worth of different types of knowledge; (ii) links between epistemic preferences and interests; (iii) barriers related to access and inclusion in governance spaces; (iv) barriers related to institutional design; and, (v) conflict‐ridden stakeholder relations. In a context of persistent epistemological conflict and distrust, we propose that knowledge co‐production focus on diplomacy through science with an aim to repair relationships rather than produce new knowledge that can serve as evidence in decision‐making as the primary goal of the co‐production process.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.033 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| 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".