Advancing Indigenous involvement in ocean monitoring through meaningful engagement and building true partnerships
Bibliographic record
Abstract
The advancement of Indigenous involvement in ocean monitoring through meaningful engagement and true partnerships are crucial for long-term monitoring and stronger data outcomes. Coastal Indigenous communities hold unique knowledge systems of the ocean, derived from generations of living in harmony with marine ecosystems. This knowledge includes local and traditional ecological knowledge (LEK and TEK) and Indigenous knowledge systems (IKS), forming the basis of localized sustainable management strategies, including for coastal fisheries. Ocean Networks Canada (ONC), an initiative of the University of Victoria, Canada, is an ocean science and technology organization, which operates and manages ocean monitoring programs located in coastal, deep-ocean, and Arctic environments. ONC strives to build meaningful, long-term partnerships with Indigenous communities which enable the successful integration of Indigenous practices into ocean monitoring. Our approach involves bridging scientific, local, and Indigenous monitoring methods to achieve a comprehensive understanding of marine ecosystems. By combining TEK, LEK, and IK systems, we can improve decision-making in environmental and resource governance. Indigenous monitoring methods, which are often qualitative and cost-effective, complement scientific approaches by providing valuable insights that traditional science may overlook. Through active engagement and collaboration with Indigenous communities, we can develop co-management strategies tailored to their socio-ecological systems, ensuring their crucial role in understanding marine ecosystem values and risks. By treating Indigenous communities as true partners in research and governance processes, we can leverage their knowledge and expertise to create more effective monitoring frameworks that benefit both communities and the environment. This inclusive approach is vital for regions where Indigenous territorial rights and governing autonomy are increasingly recognized, leading towards a more sustainable and equitable future for ocean monitoring.
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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 | 0.051 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.044 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".