Understanding school food in Newfoundland and Labrador through a systems framework
Bibliographic record
Abstract
Using systems thinking, I address the question of how to improve school food in the province of Newfoundland and Labrador (NL). Chapter Two provides an interdisciplinary review of school food literature. This review establishes the rationale for adopting systems thinking as a conceptual and analytic tool. The systems methodology used in this dissertation is described in Chapter Three. In Chapter Four I review what is known about school food in the NL context and describe the gap to be filled by using multi-method research to answer the following questions: What school food programs and policies exist in NL? What knowledge and attitudes exist about the current school food system? How do knowledge and programs interact to facilitate or inhibit development of a more healthy and sustainable school food system? Next, three research-based chapters contribute to new understanding. Chapter Five is a case study about a school greenhouse. The case study took place earlier in my PhD program and helped lead to the adoption of the systems methodology applied throughout this dissertation. Chapter Six is based on a survey of 68 principals. The results of the survey highlight the persistence of variability as a key defining feature of school food in the province and the need for more responsive and collaborative tools to assess and enhance school food systems across the province. Chapter Seven discusses findings from 34 key informant interviews of stakeholders throughout the system of school food in NL. An analysis of these interviews shows how school food system innovators drive systems change by responding to system weaknesses as a source for strategic collaboration and learning. Taken together, the findings provide a deeper understanding of how persistent and substantial barriers make interventions ineffective. Future areas for learning and collaboration are identified. I suggest that collaborative and critical knowledge about the NL school food system is essential for future transformation.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.020 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.002 | 0.005 |
| 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".