Comparing and Integrating Community‐Based and Science‐Based Approaches to Prioritizing Marine Areas for Protection
Bibliographic record
Abstract
We compared and integrated marine protected areas proposed through community and scientific assessments in 2 regions of British Columbia, Canada. The community priorities were identified during individual and group interviews with knowledgeable resource users. The scientific priorities were developed with abiotic and biotic data in Marxan, a decision-support tool. The resulting maps of community-based and science-based priorities were very similar for the inshore areas, which lent credibility to both approaches. The resource users thought the science-based maps were fairly good at highlighting areas important for conservation, but preferred the scenarios that integrated the 2 maps to either constituent map. Incorporating spatial variation in human impacts on the marine areas and commercial fishing, which are both costs of protection, into our Marxan analyses led to scenarios that were different from either constituent map. Our results show the value of integrating community-based and science-based approaches in conservation planning to achieve community acceptance and conservation utility. They also reveal that people's assessments on the basis of their traditional ecological knowledge may serve as a reasonable proxy for scientific approaches in selecting areas of ecological value.
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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 | 0.036 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".