“The Penny Lunch has Spread Faster than the Measles”: Children's Health and the Debate over School Lunches in New York City, 1908–1930
Bibliographic record
Abstract
A few days before Thanksgiving in 1908, the home economist Mabel Hyde Kittredge initiated a school lunch program at an elementary school in Hell's Kitchen, serving soup and bread to hungry children in the infamous Manhattan neighborhood. The following year, she founded the School Lunch Committee (SLC), a voluntary organization composed of home economists, educators, physicians, and philanthropists dedicated to improving the nutritional health and educational prospects of schoolchildren. By 1915, just seven years after the initiative began, the SLC was serving 80,000 free or low-price lunches a year to children at nearly a quarter of the elementary schools in Manhattan and the Bronx. Most of the schools were located in the city's poorest districts, and experience showed that the lunches were reaching those most in need at minimal cost to the organization. All the food served was inspected by the Health Department, and the meals were nutritionally balanced and tailored to the ethnic tastes and religious requirements of different school populations. Sparse but compelling evidence indicated that the program had reduced malnourishment among the children who partook, and teachers and principals at participating schools reported reductions in behavioral problems, dyspepsia, inattentiveness, and lethargy.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".