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Record W4390124462 · doi:10.1016/j.socimp.2023.100035

A non-hierarchical syncretic framework to assess environmental contaminants by interdisciplinary integration of geoscience methods and culturally-centred Indigenous knowledge exchange approaches

2023· article· en· W4390124462 on OpenAlexaffabout
Daniela Galatro, Maria Jácome, Melanie Jeffrey, V. Costanzo-Álvarez, Jason Bazylak, Cristina H. Amon

Bibliographic record

VenueSocietal Impacts · 2023
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsHumber CollegeUniversity of Toronto
Fundersnot available
KeywordsTraditional knowledgeIndigenousEnvironmental resource managementSustainabilityEnvironmental planningGeographyEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.267
Threshold uncertainty score0.656

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.067
GPT teacher head0.348
Teacher spread0.281 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes2
Has abstractyes

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