Coastal Collaboration: Exploring Emerging Frameworks to Equitably Tackle Marine Debris on the BC Coast
Bibliographic record
Abstract
Anthropogenic marine debris is plaguing the British Columbia (BC) Coast and it will take a collaborative approach to equitably tackle this issue. Outdated top-down conservation efforts do not historically provide equitable solutions to communities that are most impacted by environmental issues. A community-based lens can better reflect the disproportionate socioeconomic, cultural, and environmental burdens of marine debris. My research examines the BC Government's Clean Coast, Clean Waters initiative and the Coastal Marine Strategy as case studies to represent current and future State funding streams that support marine protection and Indigenous-led conservation. My qualitative methodology is based on participant observation, literature review, and interviews with important actors from the Province and the Kitasoo/Xai’xais First Nation. The declared Indigenous-led Marine Protected Area of Gitdisdzu Lukyeks/Kitasu Bay provides an example of asserting inherent stewardship rights in accordance with Kitasoo/Xai’xais Indigenous laws and protocols. My research questions the impacts of marine debris on biodiversity and food security, and how State policy can better support Indigenous stewardship priorities beyond recognition and remediation efforts. Co-design and co-governance strategies for Marine Protected Areas may be an indication of a shifting tide of intergovernmental relations in Canada. But only time will tell if this pivot in policy creation can provide the long-term mechanisms to equitably address 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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.032 | 0.025 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".