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Record W2041362690 · doi:10.1353/cpr.2014.0007

Reconciling Traditional Knowledge, Food Security, and Climate Change: Experience From Old Crow, YT, Canada

2014· article· en· W2041362690 on OpenAlexaboutno aff
Vasiliki Douglas, Hing Man Chan, Sonia Wesche, Cindy Dickson, Norma Kassi, Lorraine Netro, Megan Williams

Bibliographic record

VenueProgress in community health partnerships · 2014
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSustenanceFood securityAdaptation (eye)AgricultureGovernment (linguistics)Food systemsPolitical scienceEconomic growthEnvironmental resource managementClimate changeGeographyEnvironmental planningBusinessEconomicsPsychologyEcology

Abstract

fetched live from OpenAlex

BACKGROUND: Because of a lack of transportation infrastructure, Old Crow has the highest food costs and greatest reliance on traditional food species for sustenance of any community in Canada's Yukon Territory. Environmental, cultural, and economic change are driving increased perception of food insecurity in Old Crow. OBJECTIVES: To address community concerns regarding food security and supply in Old Crow and develop adaptation strategies to ameliorate their impact on the community. METHODS: A community adaptation workshop was held on October 13, 2009, in which representatives of different stakeholders in the community discussed a variety of food security issues facing Old Crow and how they could be dealt with. Workshop data were analyzed using keyword, subject, and narrative analysis techniques to determine community priorities in food security and adaptation. RESULTS: Community concern is high and favored adaptation options include agriculture, improved food storage, and conservation through increased traditional education. These results were presented to the community for review and revision, after which the Vuntut Gwitchin Government will integrate them into its ongoing adaptation planning measures.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0080.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
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 head0.286
GPT teacher head0.436
Teacher spread0.150 · 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.

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

Citations28
Published2014
Admission routes1
Has abstractyes

Explore more

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