Assessing governability of agricultural systems: Municipal agricultural planning in Metro Vancouver, Canada
Bibliographic record
Abstract
Effective governance of agricultural systems is needed for achieving goals of food security, resilient food systems, and addressing the impacts of climate change. Local governments have an increasing interest in the role of agriculture in meeting these goals. However, alignment varies greatly between local governing systems and agricultural systems. Governability is a measure of the degree to which a system can be governed for a set of specified purposes or goals. To test the limits of governability in relation to agricultural planning, we interviewed 22 agricultural planners from municipal, regional, and provincial government, and analyzed agricultural plans (n = 8) and Official Community Plans (n = 6) for six municipalities in Metro Vancouver, Canada to identify interactions between broader municipal governance, agricultural planning, and agricultural systems outcomes. Findings indicate that the governing system for agriculture in this region includes both mandated obligations (conservation of farmland) and voluntary obligations (economic development, advocacy, public awareness). Multiple limits to governing agricultural systems include the promotion and implementation of simple solutions to complex problems, limited ability to engage with the diversity of the agricultural sector and their different needs, and governance mismatches with the boundaries of agriculture (i.e., farm parcels, Agricultural Land Reserve area) and the administrative scale of the municipality. The discussion identifies specific areas where municipal governance systems could transition to improve agricultural outcomes such as farmland protection, farmer economic viability, and integration with broader food systems.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".