Local Food System Planning: The Problem, Conceptual Issues, and Policy Tools for Local Government Planners
Bibliographic record
Abstract
Local food system planning was identified in the late 1990s as an emerging and important urban planning object. Since then, little attention has been placed on identifying a robust and comprehensive understanding of the roles and tools local government can use in addressing their local food systems. The emerging literature identifi es problems with the dominant productionist agricultural system, addresses conceptual issues and often advances normative arguments in support of developing and supporting local food systems, but attention to the practical actions needed to address this issue on the ground have been limited. This paper provides an overview of the reported risks (such as water shortages, climate change, peak oil) associated with our dominant food systems, addresses the lack of attention to the importance of subscales within ‘local’ and definition of ‘local food,’ and it identifies the main reasons for considering local food systems as part of addressing the food system risks. Finally, it presents a policy framework along with tools and roles for local government to address local food systems within each of the framework’s categories. The principal purpose is to help advance the local food system work of planners in their North American communities.
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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.011 | 0.044 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".