The Beauty Underneath: Revitalizing Indigenous Shellfish Harvest in Semiahmoo Bay
Bibliographic record
Abstract
North American coastal Indigenous communities have feasted from the tidelands since time immemorial. Abundant clams, oysters, mussels, seaweed and other marine resources provided seasonal sustenance, opportunity for communities to gather, to share intergenerational knowledge, ceremony, language and cultural values. Since settler contact, land development, pollution and overharvest have diminished native oyster populations and contaminated clams and other marine resources. Current colonial coastal governance has responded with blanket shellfish harvest closures along much of the British Columbia coastline, walking away from the problem, and focusing attention on areas important for commercial harvest. This participatory action research focuses on the transboundary waters of Boundary Bay (Steloqwen in SENĆOŦEN), home of the Semiahmoo First Nation people. This place-based study reveals pathways towards revitalizing Indigenous shellfish harvest on the Canadian side of Steloqwen, through complex social-ecological system analysis and Indigenous resurgence and storytelling. While there are potential future economic benefits to Semiahmoo First Nation through the re-opening of shellfish harvesting beds, Semiahmoo are primarily interested in revitalizing their traditional practices of being out on the land, digging for clams, having opportunity for elders to share with young people about culture and language and connection to the bay. They want to restore reciprocal relationships that have been lost through contaminated waters and unjust governance. Through a literature review, jurisdictional interviews and Indigenous storytelling workshops, key barriers and opportunities were identified and discussed to reveal a pathway towards revitalizing shellfish harvest in Steloqwen. Firstly, jurisdictional roles and responsibilities related to coastal ecosystem management need to be clarified and re-defined to close existing governance gaps and to be aligned with Indigenous values. Secondly, pollution sources must be identified and controlled, which requires both a consistent, coordinated, and well-communicated monitoring plan as well as a collaborative approach to addressing the variety of potential pollution sources. And thirdly, Semiahmoo First Nation capacity must be bolstered to provide a leadership role in collaborative efforts. All three of these key barriers can be addressed by taking a two-eyed seeing approach, weaving systems analysis and Semiahmoo Indigenous teachings to reveal innovative solutions. In conclusion, this will involve: 1) ȻEN,TOEL, jurisdictional alignment to allow for Indigenous led collaborative watershed remediation, 2) ĆEĆINES, Indigenous led watershed governance with teeth to hold jurisdictions accountable, and 3) XĆETSW̱, Indigenous led collaborative process of looking over, measuring, figuring out and deciding where the pollution sources are and how best to address them. All of these solutions rely on Indigenous capacity to provide leadership and uphold their rights of sovereignty and self-determination. To operationalize this research, Semiahmoo First Nation 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. Short, medium and long-term steps are recommended to achieve the vision of revitalized shellfish harvest and a healthy bay ecosystem.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".