Coastal Collaboration: How Co-Governance Strategies and Community-based Conservation Frameworks Can Strengthen Social Equity and Tackle the Issue of Anthropogenic Marine Debris on the BC Coast
Bibliographic record
Abstract
Anthropogenic marine debris is plaguing the British Columbia (BC) coast, and it is going to take a decolonial approach to equitably tackle this issue. Outdated top-down conservation management efforts do not provide just solutions to communities that are most impacted. A community-based approach can better represent the disproportionate socio-economic, cultural, and environmental impacts of marine debris. My research explores the BC Government's Clean Coast, Clean Waters initiative as a case study to represent current and future state funding streams that can support Indigenous-led conservation. My methodology is based on interviews with important actors from the province and Indigenous communities, analysis of reports, and literature review. The Indigenous-led Marine Protected Area of Gitdisdzu Lukyeks / Kitasu Bay, in Kitasoo/Xai’xais First Nation provides an example of assertions of inherent stewardship rights. My research questions look at the impacts of marine debris on food security and how state policy can better support Indigenous stewardship priorities. I hope this research will inform ongoing collaborative efforts towards pollution prevention in the marine environment, while supporting decolonial conservation strategies. Co-design and co-governance initiatives for Marine Protected Areas may be an indication of a shifting tide of intergovernmental relations in Canada, but only time will tell if these can be the mechanism to equitably tackle the issue of marine debris on the BC Coast.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.016 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".