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Record W2160336776 · doi:10.14430/arctic240

Reducing Vulnerability to Climate Change in the Arctic: The Case of Nunavut, Canada

2009· article· en· W2160336776 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueARCTIC · 2009
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsMcGill University
Fundersnot available
KeywordsVulnerability (computing)Climate changeArcticGovernment (linguistics)Environmental resource managementEnvironmental planningGeographyCoastal erosionWildlifeResource (disambiguation)Environmental protectionErosionEnvironmental scienceOceanographyEcology

Abstract

fetched live from OpenAlex

Research conducted with the communities of Arctic Bay and Igloolik in Nunavut identified key areas where policy can help Inuit reduce their vulnerability to climate change, focusing on the renewable resource harvesting sector. The policy responses are based on an understanding of policy development and decision making and on an understanding of the processes that shape vulnerability, which in Nunavut comprise the erosion of traditional Inuit knowledge and land-based skills, the weakening of social networks, and a reduction in harvesting flexibility. Policies relating to cultural preservation, wildlife comanagement, and harvester support can serve as entry points for influencing these processes. Our recommendations fall within the mandates of the Government of Nunavut and the institutions created under the Nunavut Land Claims Agreement, and they have been identified as policy priorities by communities and Inuit organizations.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.056
GPT teacher head0.380
Teacher spread0.325 · 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