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Record W2895706883 · doi:10.15353/cfs-rcea.v5i3.260

The case for a Canadian national school food program

2018· article· en· W2895706883 on OpenAlex
Kimberley Hernandez, Rachel Engler‐Stringer, Sara Kirk, Hannah Wittman, Sasha McNicholl

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

VenueCanadian Food Studies / La Revue canadienne des études sur l alimentation · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of British ColumbiaUniversity of SaskatchewanDalhousie University
Fundersnot available
KeywordsSustainabilityFood systemsAgriculturePolitical scienceEconomic growthBusinessPublic relationsFood securityGeographyEconomics

Abstract

fetched live from OpenAlex

Canada is one of the only member countries of the Organization for Economic Cooperation and Development (OECD) without a national school food program. Good nutrition impacts children’s health, wellbeing, and learning; and school food environments offer an important setting to promote health and other food system sustainability behaviours that can last a lifetime. We present an overview of national and international evidence, with a focus on promising practices that support the establishment of a national school food program in Canada. School food programs have been shown to benefit health and dietary behaviour and critical food literacy skills (learning, culture, and social norms) that support local agriculture and promote sustainable food systems. Finally, we make recommendations for key elements that should be included in a national school food program for Canada.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
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 head0.033
GPT teacher head0.280
Teacher spread0.247 · 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