Embeddedness, Marketness, and Economic Instrumentalism in the Oklahoma Farm-to-School Program
Bibliographic record
Abstract
U.S. farm-to-school (FTS) projects and programs promote the incorporation of locally or regionally produced food, primarily fresh fruits and vegetables, in the National School Lunch Program (NSLP), a feeding program that relies on federal reimbursements and long, industrialized supply chains. FTS encourages the formation of hybrid agrifood networks that utilize shortened supply chains. This research builds upon and expands current FTS research because it examines the experiences, motivations, practices, and perceptions of farmers in a U.S. state in which FTS is facilitated by a state law. The state's promotion of FTS has reached many Oklahoma farmers through meetings with the program administrator. Some farmers have chosen to participate, while others have not. Differences in the scale of farming operations may be important in this choice. The perspectives and experiences of Oklahoma farmers vis-a-vis the state's FTS program reveals structural incompatibilities between the NSLP and FTS programs, particularly for small-scale producers. Employing the concepts of embeddedness, marketness, and economic instrumentalism, this study analyzes Oklahoma's FTS actor networks within the overarching political economy of the NSLP. It integrates literatures from alternative agrifood geographies, the sociology of agriculture, and school nutrition. Preliminary results are presented from fieldwork conducted in fall 2011 and fall 2012. Full analyses of the data will appear in future publications. Key words: farm-to-school programs, National School Lunch Program, Oklahoma Farm-to-School Program, embeddedness, marketness, economic instrumentalism
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".