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Record W1593973528 · doi:10.1111/cag.12139

Food security and health in Canada: Imaginaries, exclusions and possibilities

2014· article· en· W1593973528 on OpenAlexafffundvenueabout
Sarah Wakefield, Kaylen R. Fredrickson, Tim Brown

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2014
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDignityFood securityPolitical scienceAgricultureFood insecurityNational securityFood systemsEconomic growthSociologyPolitical economyGeographyEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract In recent years, food crises have heightened awareness of food security vulnerabilities even in rich nations. However, the extent to which various issues related to food security (such as consistent access to nutritious food in conditions which maintain human dignity) have been incorporated into Canadian policy and practice is not well documented. This article draws on a number of sources—including policy documents and media reports—to explore how food security is being conceptualized in Canada, particularly at the national level. The article chronicles changes in food security discourse over time, suggesting that recognition of food security as a “Canadian” problem has been partial and contested, and reflects persistent geographic imaginaries of Canada (e.g., as a land of agricultural abundance) and unrelenting social and cultural exclusions (e.g., of Canada's Aboriginal people).

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)0.005
metaresearch head score (Gemma)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.184
Threshold uncertainty score0.947

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0300.034
Scholarly communication0.0150.003
Open science0.0020.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.000

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 head0.038
GPT teacher head0.300
Teacher spread0.262 · 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

Citations26
Published2014
Admission routes4
Has abstractyes

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