Integrating Family Farming into School Feeding: A Systematic Review of Challenges and Potential Solutions
Bibliographic record
Abstract
Family farming is strengthening its strategic role in school nutrition, but coordinating between school feeding programs and the agricultural sector has proven to be challenging. The goal of this review was to identify the problems that school feeding programs face in acquiring food from family farms. We selected studies from Web of Science, Medline/PubMed, and Scopus and evaluated their methodological quality. Out of 338 studies identified, 37 were considered relevant. We used PRISMA to guide the review process, and we chose not to limit the year or design of the study because it was important to include the largest amount of existing evidence on the topic. We summarized the main conclusions in six categories: local food production, marketing, and logistics channels, legislation, financial costs, communication and coordination, and quality of school menus. In general, the most critical problems emerge from the most fragile point, which is family farming, particularly in the production and support of food, and are influenced by the network of actors, markets, and governments involved. The main problems stem from the lack of investment in family farming and inefficient logistics, which can negatively impact the quality of school meals. Viable solutions include strategies that promote investment in agricultural policies and the organization of family farmers.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".