Co-management at a crossroads in Canada: issues, opportunities, and emerging challenges in fisheries and marine contexts
Bibliographic record
Abstract
Despite some progress, successful co-management in Canada has remained the exception rather than the rule, and especially so in jurisdictions not covered by a comprehensive land claims agreement. As such, our aim in this perspective is to identify and describe some of the primary factors that may impede more rapid progress toward successful co-management and to explore why they persist, with particular attention to fisheries and marine contexts. Specifically, we outline several institutional conditions that are likely to impede broader adoption of co-management approaches in Canada, including (1) antiquated and incomplete legislative arrangements; (2) a co-management policy vacuum that has not grappled with emerging expectations for co-governance; (3) relative absence of the knowledge co-production systems needed to create the precursors for successful co-management initiatives; and (4) financial and human resource capacity limitations. Such conditions must also be situated in a dynamic context that includes the United Nations Declaration of the Rights of Indigenous Peoples, ongoing reconciliation processes, and shifts in the ownership and use of fisheries and other marine resources. We offer, finally, some suggestions to augment co-management efforts and ultimately achieve its promise.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.046 | 0.016 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".