Resilience-based steps for adaptive co-management of Arctic small-scale fisheries
Bibliographic record
Abstract
Abstract Arctic small-scale fisheries are essential for the livelihoods, cultures, nutrition, economy, and food security of Indigenous communities. Their sustainable management in the rapidly changing Arctic is thus a key priority. Fisheries management in complex systems such as the Arctic would benefit from integrative approaches that explicitly seek to build resilience. Yet, resilience is rarely articulated as an explicit goal of Arctic fisheries management. Here, we first describe how marine and anadromous fisheries management throughout the North has used the notion of resilience through a literature review of 72 peer-reviewed articles. Second, we make a conceptual contribution in the form of steps to implement adaptive co-management that aim to foster resilience. Building on resilience-based insights from the literature review and foundational research on adaptive co-management and resilience, the steps we propose are to initiate and carry out (1) dialogue through a discussion forum, (2) place-based social-ecological participatory research, (3) resilience-building management actions, (4) collaborative monitoring, and (5) joint process evaluation. Additionally, we propose action items associated with the steps to put adaptive co-management into practice. Third, we assess two case studies, Cambridge Bay and Pangnirtung Arctic Char commercial fisheries, to explore how the five steps can help reinforce resilience through adaptive co-management. Overall, we propose novel guidelines for implementing adaptive co-management that actively seeks to build resilience within fishery social-ecological systems in times of rapid, uncertain, and complex environmental change.
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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 | 0.040 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".