Indigenous Traditional Ecological Knowledge and Ocean Observing: A Review of Successful Partnerships
Bibliographic record
Abstract
Understanding and management of the marine environment requires respect for, and inclusion of, Indigenous knowledge, cultures, and traditional practices. The Aha Honua, an ocean observing declaration from Coastal Indigenous Peoples, calls on the ocean observing community to “formally recognize the traditional knowledge of Indigenous peoples,” and “to learn and respect each other’s ways of knowing.” Ocean observing systems typically adopt open data sharing as a core principle, often requiring that data be Findable, Accessible, Interoperable, and Reusable (FAIR). Without modification, this approach to Traditional Ecological Knowledge (TEK) would mean disregarding historical and ongoing injustices and imbalances in power, and information management principles designed to address these wrongs. Excluding TEK from global ocean observing is not equitable or desirable. Ocean observing systems tend to align with settler geography, but their chosen regions often include Indigenous coastal-dwelling communities that have acted as caretakers and stewards of the land and ocean for thousands of years. Achieving the call of Aha Honua will require building relationships that recognize Indigenous peoples play a special role in the area of ocean stewardship, care, and understanding. This review examines the current understanding of how Indigenous TEK can be successfully coordinated or utilized alongside western scientific systems, specifically the potential coordination of TEK with ocean observing systems. We identify relevant methods and collaborative projects, including cases where TEK has been collected, digitized and the meta(data) has been made open under some or all the FAIR principles. This review also highlights enabling factors that notably contribute to successful outcomes in digitization, and mitigation measures to avoid the decontextualization of TEK. Recommendations are primarily value- and process-based, rather than action-based, and acknowledge the key limitation that this review is based on extant written knowledge. In cases where examples are provided, or local context is necessary to be concrete, we refer to a motivating example of the nascent Atlantic Regional Association of the Canadian Integrated Ocean Observing System and their desire to build relationships with Indigenous communities.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".