Change in School Nutrition–Related Laws From 2003 to 2008: Evidence From the School Nutrition–Environment State Policy Classification System
Bibliographic record
Abstract
OBJECTIVES: We examined state laws affecting the school food environment and changes in these laws between 2003 to 2008. METHODS: We used the Westlaw legal database to identify state-codified laws, with scoring derived from the updated School Nutrition-Environment State Policy Classification System, obtained from the Classification of Laws Associated With School Students Web site. RESULTS: States significantly changed their school nutrition laws from 2003 to 2008, and many increased the stringency of the laws targeting competitive foods (snacks and entrées sold in competition with the school meal) and beverages sold in school and for in-school fundraising. Many states enacted laws that mandated the establishment of a coordinating or advisory wellness team or council. Stronger laws were enacted for elementary grades. We found tremendous variability in the strength of the laws and plenty of room for improvement. CONCLUSIONS: State law governing school nutrition policies significantly changed from 2003 to 2008, primarily affecting the competitive food environment in schools. The extent to which changes in school nutrition laws will lead to desired health outcomes is an area for additional research.
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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".