FOOD PRODUCTION SYSTEMS, POLICIES AND RURAL PLANNING: CONTRIBUTIONS TO SUSTAINABILITY AND ENVIRONMENTAL IMPACT
Bibliographic record
Abstract
A large portion of the most agriculturally-viable land in Canada is located in the province of Ontario. Within Ontario, municipal governments are the mechanism by which provincial land-use policy is implemented, and virtually all agricultural production happens within the boundaries of an upper-tier municipal government. This means that municipal governments are the most local level of government responsible for making decisions and implementing programs and policies related to the agriculture and agri-food sector. However, little is known about the structure, knowledge base, and capacity of municipal governments to respond to agricultural and agri-food priorities and issues. This paper reveals that the capacity of county planning departments is varied and presents a case for further research on this topic. The agricultural and agri-food sector is in a position where it both contributes and is extremely vulnerable to climate change; expertise is needed to manage both the risks and opportunities that rural communities face. It is imperative that governments and decision-makers who affect the agriculture and agri-food industry have capacity and knowledge to support the sector and respond to critical issues as they arise. The decisions of elected officials, the resources that municipalities have, and the expertise of staff are all key elements that affect implementation of provincial priorities and the consideration given to agriculture when creating policies, programs, and initiatives.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".