Bibliographic record
Abstract
The purpose of this research study is to understand school food programs and how they are implemented. My research questions are: What is a school food program [SFP]; why are SFP implemented; who are the stakeholders in a SFP; what are the current ways of implementing food programs into schools; What factors influence whether or not school food programs are successful; and how can barriers to SFP be addressed. This study is an extended literature review using a combination of peer-reviewed articles, news releases and government websites as well as government-supported organization’s websites. I chose to conduct an integrative literature review in order to determine a suitable framework for school food programs in rural Saskatchewan. I approached this integrative literature review using an ecological research approach consisting of both qualitative and quantitative data. The body of this literature review seeks to answer the six research questions to form an overall viewpoint of school food programs. Within these research questions, themes of food insecurity, national nutritional concerns, social and academic benefits for students, global SFPs, nutritional policies, community organizations and volunteers, and funding emerge. The paper results in an implementation planning checklist created to assist home economists, teachers, and community members to start a SFP. A reflection, summary and recommendation from a home economics teacher and previous SFP implementer concludes this study.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".