Bibliographic record
Abstract
This article examines the political economy of nutrition as a state-sponsored strategy to extract greater productivity from industrial workers in both wartime and peacetime. During World War II, the state, together with its munitions-industry allies, broadly considered workers’ nutritional health as a critical component to achieving maximum wartime industrial production. Following the war, both the state and industry imagined the nutritional health of workers’ bodies as crucial to Canada’s postwar prosperity. Facilitating as well as frustrating these largely state-directed nutrition agendas was a combination of medico-scientific knowledge, the sometimes uncertain and unpredictable participation of both employers and workers, and wider national and international historical contexts. Cet article examine l’économie politique de la nutrition en tant que stratégie parrainée par l’État pour accroître la productivité des travailleurs industriels, en temps de guerre comme en temps de paix. Au cours de la Seconde Guerre mondiale, l’État et ses alliés de l’industrie des munitions ont largement considéré la santé nutritionnelle des travailleurs comme un élément essentiel pour parvenir à une production industrielle maximale en temps de guerre. Après la guerre, l’État et l’industrie ont tous deux estimé que la santé nutritionnelle des corps des travailleurs était essentielle à la prospérité du Canada d’après-guerre. Faciliter mais aussi frustrer ces programmes nutritionnels largement dirigés par les États était une combinaison des connaissances médicales et scientifiques, une participation parfois incertaine et imprévisible des employeurs et des travailleurs, et des contextes historiques nationaux et internationaux plus vastes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".