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Record W2163358750 · doi:10.1017/s1466046600002325

Environmental Review: Arctic Contaminants and Country Foods: Scientific and Indigenous Perspectives on Environmental Risks

2001· article· en· W2163358750 on OpenAlexaffabout
Stephen Bocking

Bibliographic record

VenueEnvironmental Practice · 2001
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsTrent University
Fundersnot available
KeywordsIndigenousCircumpolar starArcticEnvironmental planningOpenness to experienceWildlifeEnvironmental protectionPolitical scienceEnvironmental resource managementBusinessGeographyPsychologyEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

The risks posed by environmental contaminants in the Arctic are becoming increasingly recognized by circumpolar nations, and research programs are now addressing these risks. Of particular concern is the presence of contaminants in wildlife—a food source of nutritional, cultural, social, and economic importance to northern indigenous peoples. This article reviews recent environmental health research in the Canadian Arctic, focusing on the development of advice regarding the consumption of “country foods.” Such advice must be in appropriate, non-technical formats, emphasizing face-to-face communication, and designed to enable northern people to participate in decisions regarding consumption of country foods. More generally, assessing and communicating health risks posed by northern contaminants presents several distinctive challenges that reflect the cultural gulf between northerners, and scientists and professionals within health agencies. Bridging this gulf will require effective communication skills, the building of relationships of trust, sensitivity to local conditions and concerns, and an openness to alternative perspectives on the natural environment.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.391
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0070.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.348
Teacher spread0.320 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations2
Published2001
Admission routes2
Has abstractyes

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