Protecting Repositories of Indigenous Traditional Ecological Knowledges: A Health-Focused Scoping Review
Bibliographic record
Abstract
Indigenous Peoples have stewarded Indigenous traditional ecological knowledges (TEK) for millennia. Health-related TEK represents vital knowledge that promotes Indigenous health and wellbeing. Yet, the intergenerational protection of TEK continues to be threatened by various factors, including climate change, which underscores the importance of strengthening and supporting Indigenous-managed TEK repositories. Using a scoping review methodology, we aimed to identify documents for setting up health-related TEK repositories within Indigenous communities. A systematic search was completed in multiple databases-Medline, PubMed, CABI abstracts, Canadian Public Policy Collection, and JSTOR-with manual searches carried out on relevant Indigenous repositories and Google. Content analysis was then carried out with the nine documents meeting our inclusion criteria. We characterized six overarching categories and twelve sub-categories from the included documents. These categories covered impacts on Indigenous TEK repositories resulting from colonial processes, with TEK being seen as diverse, living knowledge protected by longstanding cultural protocols. Concerns surrounding TEK repository management included the need for platforming Indigenous data sovereignty and Indigenous Peoples' access and ownership. Wise practices of Indigenous-led repository development demonstrated clear examples of data governance processes in action. Indigenous communities were seen to be vital in contributing to key policies and protocols that protect health-related TEK.
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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 | 0.027 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.026 | 0.025 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".