Knowledge Connects Our Hearts and Lands: A Qualitative Research Study on Stewarding Indigenous Traditional Ecological Knowledges for Community Well-Being
Bibliographic record
Abstract
Indigenous Peoples have developed and stewarded complex knowledge systems that have contributed to thriving societies. With continued threats to Indigenous lifeways, there is increasing need to further protect traditional ecological knowledges (TEK). We carried out a qualitative study to explore Indigenous community perspectives on stewarding and protecting TEK while identifying gaps in community-level protections of TEK. We conducted ten semi-structured interviews in December 2024 and one focus group in January 2025 with Indigenous Peoples in the southwestern United States. Reflexive thematic analysis through open coding was carried out using qualitative software. Six overarching themes were characterized in the interviews, which overlapped with findings from the focus group, including the following: (1) Historical and current barriers impact the sharing of TEK; (2) Preserving our language is necessary for intergenerational transmission of our TEK; (3) Our TEK reveals changes to our Lands; (4) Protecting our Lands and medicines is vital to our health; (5) We must take the time to learn our TEK for future generations; and (6) We need to protect our TEK. Our research highlights the importance of supporting Indigenous communities' capacities to protect their TEK for personal, community, and environmental well-being.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.019 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".