Native American Perspectives on Health and Traditional Ecological Knowledge
Bibliographic record
Abstract
BACKGROUND: Traditional ecological knowledge (TEK) is a conceptual framework that highlights Indigenous knowledge (IK) systems. Although scientific literature has noted the relevance of TEK for environmental research since the 1980s, little attention has been given to how Native American (NA) scholars engage with it to shape tribal-based research on health, nor how non-Native scholars can coordinate their approaches with TEK. This coordination is of particular importance for environmental health sciences (EHS) research exploring interdisciplinary approaches and the integration of environmental and human health. OBJECTIVE: Our perspective on TEK arose from a series of Health and Culture Research Group (HCRG) workshops that identified gaps in existing EHS methodologies that are based on a reliance on Euro-American concepts for assessing environmental exposures in tribal communities. These prior methods neither take into account cultural behavior nor community responses to these. Our objective is to consider NA perspectives on TEK when analyzing relationships between health and the environment and to look at how these may be applied to address this gap. DISCUSSION: The authors—the majority of whom are NA scholars—highlight two research areas that consider health from a TEK perspective: food systems and knowledge of medicinal plants. This research has yielded data, methods, and knowledge that have helped Indigenous communities better define and reduce health risks and protect local natural food resources, and this TEK approach may prove of value to EHS research. CONCLUSION: NA perspectives on TEK resulting from the HCRG workshops provide an opportunity for developing more accurate Indigenous health indicators (IHI) reflecting the conceptualizations of health maintained in these communities. This approach has the potential to bridge the scientific study of exposure with methods addressing a tribal perspective on the sociocultural determinants of health, identifying potential new areas of inquiry in EHS that afford nuanced evaluations of exposures and outcomes in tribal communities. https://doi.org/10.1289/EHP1944.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.029 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".