Biomonitoring and Ethnobiology: Approaches to Fill Gaps in Indigenous Public and Environmental Health
Bibliographic record
Abstract
Ethnobiology is well positioned to work in tandem with biomonitoring research to create a more complete understanding of how people experience and are affected by contaminated environments. Indigenous communities in proximity to unconventional natural gas (“fracking”) facilities face potential health risks that are often poorly assessed or not assessed at all. This contribution reviews a biomonitoring pilot research project in British Columbia (Canada) that was informed by Indigenous Peoples' concerns of contaminant exposure from traditional foods and their environment. Preliminary biomonitoring results indicate higher levels of a benzene metabolite in pregnant Indigenous women near fracking facilities, compared to what measured in non-Indigenous women. We investigate how Indigenous Peoples' concerns of exposure to industrial contaminants should inform biomonitoring and toxicological studies and, conversely, how biomonitoring studies can complement ethnobiological research with assessable data. By focusing on environmental knowledge and human health in the context of oil and gas development, we critically evaluate how action, environmental justice, and scientific research can and should contribute to more ethical and methodological frameworks and practices. Together, ethnobiology and biomonitoring can be used to fill in important knowledge gaps in environmental health and ethical research practices.
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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 | 0.038 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.013 | 0.053 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.004 | 0.005 |
| 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".