Pacific Canada's Rockfish Conservation Areas: Using Ostrom's Design Principles to Assess Management Effectiveness
Bibliographic record
Abstract
"International declines in marine biodiversity have lead to the creation of marine protected areas and fishery reserve systems. In Canada, 164 Rockfish Conservation Areas (RCAs) were implemented between 2003 and 2007 and now cover 4847.2 km² of ocean. These reserves were created in response to widespread concern from fishers and nongovernmental organizations about inshore rockfish (genus Sebastes) population declines. We used the design principles for effective common-pool resource management systems, originally developed by Elinor Ostrom, to assess the social and ecological effectiveness of these conservation areas more than 10 years after their initial implementation. We assessed the relative presence or absence of each design principle within current RCA management. We found that 2 of the 11 design principles were moderately present in the recreational fishery. All other design principles were lacking for the recreational sector. We found that 2 design principles were fully present and 5 were moderately present in the commercial sector. Four design principles were lacking in the commercial sector. Based on this analysis, we highlight 4 main areas for management improvement: (1) create an education and outreach campaign to explain RCA rules, regulations, boundaries, and the need for marine conservation; (2) increase monitoring of users and resources to discourage noncompliance and gather the necessary data to create social buy-in for marine conservation; (3) encourage informal nested governance through stakeholder organizations for education and self-regulation (e.g. fisher to fisher); and (4) most importantly, create a formal, decadal RCA review process to gather stakeholder input and make amendments to regulations and RCA boundaries. This information can be used to inform spatial management systems both in Canada and internationally. This analysis also contributes to a growing literature on effectively scaling up small-scale management techniques for large-scale, often federally run, common-pool resource systems."
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".