Bibliographic record
Abstract
This session will reflect on the importance of rural and agri-food communities and provide key insights on the capacity of municipal governments to support the agriculture and agri-food sector and respond to rural issues. In Ontario, where the most agriculturally-viable land in Canada is located, municipalities are the most local level of government and are responsible for implementing provincial and federal policies and directives. However, little is known about the structure, knowledge base, and capacity of municipal governments to respond to agricultural and agri-food priorities and issues. A review of existing literature and municipal websites reveals that municipal planning departments are extremely varied and inconsistently staffed. This appears to be the surface of a much larger inconsistency related to financial resources, staff expertise, and council’s knowledge about agriculture and agri-food. Our team has completed the research related to this project and the findings of this study are informed by data collected via survey and semi-structured interviews from 66 municipalities in the Greenbelt. Findings indicate that there is an increasing knowledge gap related to agricultural planning and agri-food issues, and that fewer elected officials/planners have agricultural backgrounds, expertise, or training. This presents a threat to rural and agri-food communities as their livelihoods depend on the ability of council (and staff) to understand critical issues, protect farmland, and make agriculturally-supportive decisions. Agri-food communities are important and it is critical that our governance systems not only recognise that but also have the capacity to support, protect, and respond to the agri-food sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".