First Nations Environmental Contaminants Program: Integrating existing knowledge on potential contaminated sites within the Nation
Bibliographic record
Abstract
Indigenous communities are disproportionately affected by chemical contamination due to the colonial policies that have contributed to inequitable management practices and lack of access to decision-making processes. Dominant environmental assessment frameworks pertaining to chemical contamination are limited to assessing physiological implications of chemical substances on living organisms and exclude the crucial mental, cultural, sociological, and political dimensions of chemical contamination, and they notably do not consider the effects of contamination on the complex bond between Indigenous peoples and their lands. This interdisciplinary research in collaboration with the Oneida Nation of the Thames aims to integrate scientific data and community-based knowledge sources on risk assessment and risk communication of chemical exposure. The project has two objectives: to assess the contaminants present on the lands of the Oneida Nation and to construct an all-inclusive knowledge integration platform to mobilize contamination-related knowledge. The second objective is designed towards community engagement, prioritizing youth participation and empowerment through knowledge sharing and skills enhancement. This exploratory research is projected to address uncertainty related to potential contamination which limits future community planning. On a broader scale, it will inform environmental assessment frameworks pertaining to chemical contamination to include Indigenous perspectives which will expand theoretical dimensions of current frameworks toward more robust practical outcomes. Funding: FNECP, RBC, GIER, SSHRC
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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 | 0.003 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".