Setting nutritional standards theory, policies, practices
Bibliographic record
Abstract
Suzanne Junod's essay "Proscribing Deception": The Gould Net Weight Amendment and the Origins of Mandatory Nutrition Labeling" is the winner of the 2017 Charles Thomson Prize of the Society for the History of the Federal Government. In the second half of the nineteenth century, ways of thinking about food changed as chemists and physiologists identified nutrients and bodily needs and as urbanization, industrialization, and colonial encounters challenged traditional dietary customs and assumptions. Emerging as a reaction to concerns about industrial and military power, social welfare, and public health, the science of nutrition sought to define the norms and needs of variable human bodies, setting standards for bodies and foods that would enable physicians and politicians to develop nutritional recommendations and food policies for individuals and populations. Setting Nutritional Standards brings together authors from a variety of disciplines to explore perspectives on the theory, practices, and policies of modern nutrition science from the 1860s to the 1960s. The essays place the new science of nutrition within the changing social landscapes of Western Europe and the United States at the intersection of medicine, policy, social reform agendas, and public health initiatives. CONTRIBUTORS: Nick Cullather, Suzanne Junod, Deborah Neill, Elizabeth Neswald, David F. Smith, Ulrike Thoms, Corinna Treitel, Ina Zweiniger-Bargielowska Elizabeth Neswald is associate professor for the history of science and technology at Brock University, Canada. David F. Smith is Honorary Senior Lecturer in the history of medicine at the University of Aberdeen. Ulrike Thoms is a historian of science and researcher at the Max Planck Institute for the History of Science
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 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 | 0.049 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.010 | 0.077 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".