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Record W2189268167 · doi:10.22605/rrh1370

Observations of environmental changes and potential dietary impacts in two communities in Nunavut, Canada

2010· article· en· W2189268167 on OpenAlexafffundabout
Tanya Nancarrow

Bibliographic record

VenueRural and Remote Health · 2010
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsUniversity of Northern British ColumbiaMcGill University
FundersArcticNet
KeywordsGeographyEnvironmental protectionEnvironmental impact assessmentEnvironmental planningEnvironmental healthEnvironmental resource managementEnvironmental scienceEcologyMedicineBiology

Abstract

fetched live from OpenAlex

INTRODUCTION: Inuit from communities across the Arctic are still existing in subsistence living. Hunting, fishing and gathering is an important part of the culture and the harvested 'country food' provides sources of nutrients invaluable to maintaining the health of the populations. However, Inuit are voicing their concerns on how observed climate change is impacting on their traditional life. The objective of this study was to report on observed climate changes and how they affect the country food harvest in two communities in the Canadian Arctic. The nutritional implications of these changes are discussed and also how the communities need to plan for adaptations. METHODS: A total of 17 adult participants from Repulse Bay and Kugaaruk, Nunavut were invited to participate. Participants were selected using purposeful sampling methods selecting the most knowledgeable community members for the study. Inuit Elders, hunters, processors of the animals, and other community members above the age of 18 years were selected for their knowledge of harvesting and the environment. Two-day bilingual focus groups using semi-directed, unstructured questions were held in each community to discuss perceived climate changes related to the access and availability of key species. Key topics of focus included ice, snow, weather, marine mammals, land mammals, fish, species ranges, migration patterns, and quality and quantity of animal populations. Maps were used to pinpoint harvesting locations. A qualitative analysis categorizing strategy was used for analysis of data. This strategy involves coding data in order to form themes and to allow for cross-comparison analysis between communities. Each major animal represented a category; other categories included land, sea, and weather. Results were verified by the participants and community leaders. RESULTS: Three themes emerged from the observations: (1) ice/snow/water; (2) weather; and (3) changes in species. Climate change can affect the accessibility and availability of the key species of country foods including caribou, marine mammals, fish, birds and plants. Various observations on relationship between weather and population health and distributions of the animal/plant species were reported. While many of the observations were common between the two communities, many were community specific and inconsistent. Participants from both communities found that climate change was affecting the country food harvest in both positive and negative ways. Key nutrients that could be affected are protein, iron, zinc, n-3 fatty acids, selenium and vitamins D and A. CONCLUSION: Community members from Repulse Bay and Kugaaruk have confirmed that climate change is affecting their traditional food system. Local and regional efforts are needed to plan for food security and health promotion in the region, and global actions are needed to slow down the process of climate change.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.205

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0140.002
Scholarly communication0.0020.000
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.327
Teacher spread0.293 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations47
Published2010
Admission routes3
Has abstractyes

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