“Coastal Voices”: creating resilient, reciprocal human-ocean relationships by upholding Indigenous laws through knowledge co-production and collaborative action.
Bibliographic record
Abstract
To secure a safe and just future for our oceans and generations to come, Coastal Voices, a transdisciplinary research collective, upholds and practices science and conservation that is revitalizing ancient Indigenous laws and knowledge systems. We are accountable to, and uphold the rights and responsibilities of, Indigenous Hereditary Leaders and their Nations along the West Coast of Canada. Coastal Voices was born out of the need to replace present colonial ocean policies and governance arrangements that have been unsuccessful at supporting resilient coastal social-ecological relationships. In contrast, ancestral Indigenous laws better reflect interlinked social and ecological processes and objectives that are place-based yet nested within regional governance systems. A Steering Committee of Indigenous Hereditary leaders, representing Chiefs’ Councils of the nuučaanuɫ, Haíɫzaqv and Xaayda Nations, guides Coastal Voices, our collective of cultural advisors, practitioners, artists, and a research team of natural and social settler scientists and Indigenous scholars. Together, we examine how the revitalization of Indigenous laws, such as respect, responsibility and balance, and stewardship practices, such as sea gardens and hunting, can support food sovereignty, cultural identity, and biodiversity conservation amid the recovery of a keystone predator, the sea otter, and a changing ocean climate. Coastal Voices strives to live our words by putting research results and stewardship practices into action. In summer 2024 we helped build a clam garden, a type of ancestral sea garden, that increases production of clams by buffering them from climate extremes. To inform continued care-taking actions in the future, we also co-created a simulation model that allows us to compare how sea otter-human-shellfish relationships may change under alternative stewardship scenarios. Through an iterative process of workshops, witnessing, interviews, and validation with Indigenous leaders, we identified diverse objectives and responsibilities of Indigenous leaders, and brought in empirical data to create a model rooted in place-based Indigenous knowledge systems, the ancient laws of balance, respect, and interconnectedness, and western science. By upholding ancestral Indigenous authority, protocols, and laws, our Coastal Voices approach reflects an ancient and unique way to create equitable, relevant science that aligns with the United Nations Declaration on the Rights of Indigenous People (UNDRIP) and supports Nation-to-Nation environmental governance and knowledge mobilization processes. This approach elevates Indigenous traditions, knowledge, and governance authority in science and its application in ocean decision-making.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".