Centering community values in marine planning
Bibliographic record
Abstract
Centering community values in conservation decision-making can mitigate harm to social-ecological systems caused by the climate, biodiversity, and social justice crises. However, it can be unclear how to weave these values into complicated processes, such as marine spatial planning (MSP), that have historically favoured western and biophysical knowledge and can perpetuate inequitable and status-quo power dynamics. Here, we contribute a community-led approach to create knowledge in support of MSP that works to center local and Indigenous values. Indigenous, academic, and non-profit partners co-created a mixed-methods participatory mapping approach to characterize place-based values within a fjord in the Salish Sea. We conducted 30 interviews and 300 surveys to map ocean-based values and characterize interactions across values (e.g., perceptions of conflicts and compatibilities). Communities strongly supported ecological values and identified places where spatial conservation and management opportunities could be explored with minimal perceived trade-offs. Results were shared with a community MSP decision-support tool to improve data accessibility and bridge the gap between knowledge and action. This mixed-methods approach can be replicated in other coastal communities to elevate the inclusion of social and cultural data in MSP and enhance harmonization of planning processes across governance scales (e.g., Indigenous, local, provincial, federal, transboundary). Overall, this case study contributes a local and Indigenous-partnered approach that centers community values and knowledge in early MSP stages so that both ocean and community health are meaningfully protected.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.026 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".