A non-hierarchical syncretic framework to assess environmental contaminants by interdisciplinary integration of geoscience methods and culturally-centred Indigenous knowledge exchange approaches
Bibliographic record
Abstract
Different soil and water pollution sources around an undisclosed First Nation (The FN) in Northern Ontario (Canada) are linked by community health documents and oral histories to a cluster of blood cancer. The site's environmental hydrogeochemical records available are difficult to follow, whereas geophysical data reveals possible pathways of contaminants displayed as 3D maps of subsoil contrasting geoelectrical properties. Through an Indigenous Integrated Knowledge Translation (IIKT) strategy, we have co-constructed with The FN an interdisciplinary framework of non-hierarchical syncretic exchange between geoscience-based environmental engineering praxes and Indigenous Knowledge. The IIKT is articulated through Talking Circles of flexible multidirectional exchanges between The FN and the research team, to address community-identified needs and maintain qualitative and contextual value in the investigative agenda. The Talking Circles have guided our efforts to collect, handle, integrate, and understand hydrogeochemical and geophysical data. Thus, we build a culturally-appropriate knowledge base for self-sufficient environmental monitoring capacities with the community to ensure informed decisions about the land. The sustainability of the proposed framework relies on the non-invasiveness and low cost of the environmental/engineering tools used, the transparency of the community-driven results obtained, and its scalability to other Indigenous communities.
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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 | 0.021 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".