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Record W1969870236 · doi:10.15353/cfs-rcea.v1i2.46

Food Will Win the War: The Politics, Culture, and Science of Food on Canada’s Home Front

2014· article· en· W1969870236 on OpenAlex
Jennifer Brady

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 · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsQueen's University
Fundersnot available
KeywordsPoliticsFront (military)Spanish Civil WarHome frontWorld War IIResource (disambiguation)Political scienceEconomic historySociologyHistoryGeographyLaw

Abstract

fetched live from OpenAlex

When most of us think of Canadian history, particularly Canada’s involvement in the Second World War, it is unlikely that food is what first comes to mind. However, Ian Mosby’s new—and first—book, Food Will Win the War: The Politics, Culture, and Science of Food on Canada’s Home Front, invites readers to consider the primacy of food in the war effort in Canada. Mosby’s detailed and thoroughly researched account explores food as a material and symbolic resource that was instrumental in marshalling Canadians’ support for the war. Mosby also shows how the social, political, and economic changes related to food shaped the everyday lives of Canadians—particularly Canadian women—throughout the Second World War. Food Will Win the War is an important volume that fills a significant gap in the small, but growing, literature on Canada’s food history.

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 categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
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 studies0.0040.003
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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