Insights into the government's role in food system policy making: improving access to healthy, local food alongside other priorities
Bibliographic record
Abstract
Government actors have an important role to play in creating healthy public policies and supportive environments to facilitate access to safe, affordable, nutritious food. The purpose of this research was to examine Waterloo Region (Ontario, Canada) as a case study for “what works” with respect to facilitating access to healthy, local food through regional food system policy making. Policy and planning approaches were explored through multi-sectoral perspectives of: (a) the development and adoption of food policies as part of the comprehensive planning process; (b) barriers to food system planning; and (c) the role and motivation of the Region’s public health and planning departments in food system policy making. Forty-seven in-depth interviews with decision makers, experts in public health and planning, and local food system stakeholders provided rich insight into strategic government actions, as well as the local and historical context within which food system policies were developed. Grounded theory methods were used to identify key overarching themes including: “strategic positioning”, “partnerships” and “knowledge transfer” and related sub-themes (“aligned agendas”, “issue framing”, “visioning” and “legitimacy”). A conceptual framework to illustrate the process and features of food system policy making is presented and can be used as a starting point to engage multi-sectoral stakeholders in plans and actions to facilitate access to healthy food. Keywords: food policy; community food security; public health; government; land use planning; food access
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".