The beauty underneath: A critical coastal governance approach to revitalize indigenous shellfish harvest
Bibliographic record
Abstract
Indigenous communities have feasted from their tidelands since time immemorial. Yet today, many are disconnected due to land development, pollution, overharvest, and colonial law. Coastal First Nations live within the reality of their inherent laws and ways of being, held in tension with the restrictions and confusion of Canadian laws, funding models and bureaucracies. There is a need to bring enriched understanding and clarity to coastal ecosystems governance to address the overlapping crises of biodiversity loss, climate change, and food insecurity. Indigenous-led governance could provide a way forward. Featured through the example of shellfish management in Boundary Bay, Canada, this participatory action research study applies a critical coastal governance lens and systems thinking approach to evaluate existing colonial governance frameworks and complex interjurisdictional, intertidal relationships. By amplifying Indigenous voices and knowledge from a specific coastline through community filmmaking and story mapping, current gaps and dysfunction are addressed. Our analysis reveals that to operationalize this approach to coastal governance, Indigenous communities must be acknowledged and supported as leaders with the capacity and authority to carry out governance in their territory, influencing land and water management on a watershed scale. While legal resistance tools are effective in some cases to address issues of Aboriginal rights and title, collaborative approaches can be more effective. We elaborate short, medium and long-term recommendations to achieve a revitalized shellfish harvest and healthy bay ecosystem by centering Indigenous-led coastal governance. • Barriers to revitalizing shellfish harvest include: jurisdictional complexity, polluted runoff, and capacity constraints. • Indigenous-led watershed governance can shift paradigms within Canadian shellfish jurisdictional frameworks. • Indigenous-led collaborative remediation and monitoring rebuilds reciprocal relationships and enables food sovereignty.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.040 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".