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Record W4285470406 · doi:10.22215/cpopp.v7i.3180

Of Kale and Caribou

2021· article· en· W4285470406 on OpenAlexaboutno aff
Lauren Kathryn Johnson

Bibliographic record

VenueCarleton Perspectives on Public Policy · 2021
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsFood insecurityGovernment (linguistics)Food securityConsumption (sociology)HarmBusinessScale (ratio)Economic growthPolitical scienceGeographyEconomicsAgricultureSociology

Abstract

fetched live from OpenAlex

Rates of food insecurity in Nunavut are the highest in Canada. This paper will focus on the federal government’s role in solving this problem, arguing that a thorough examination of the coherence of existing arrangements, institutions, programs, and regulations that impact food insecurity in Nunavut is needed. As an example, this paper will focus on three factors that contribute to food insecurity among the Inuit of Nunavut and policies that seek to address them or that exacerbate them: 1) high prices of food sourced from southern Canada, and the efficacy of the Government of Canada’s program, Nutrition North Canada, in decreasing those prices; 2) decreased rates of country food consumption and possible programming to increase that consumption; and 3) the role of large-scale infrastructure projects in damaging the Inuit traditional food system, followed by recommendations for the types of infrastructure projects that would strengthen food security, rather than harm it.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.546

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.039
GPT teacher head0.386
Teacher spread0.347 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2021
Admission routes1
Has abstractyes

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