Sustainable urban food districts (SUFD): Strategical spatial planning in urban food systems. An analysis to the Toronto food strategy policy
Bibliographic record
Abstract
The recognition of a historical absence on urban food systems analysis by the academia, incentivized new discussion on planners and food activist only over the past 15 years.The predominant belief on food as an agricultural and rural issue, has fostered its detachment from the urban agenda, filling the gap with predominant market driven strategies.This paper aims to provide a comprehensive vision on the food system organization in urban areas, analysing the need to integrate it into a broader urban strategy and strategical spatial planning.The challenges and opportunities it portrays the need to consolidate competitive and sustainable solutions for an increasing urban population and their connected social challenges.Thus, comprehended into a broader spectrum of issues of public concern such as health, social justice, economic prosperity, social cohesion, food security, culture, waste management and ecological integrity.The paper includes the analysis of a case study, the Toronto Food Strategy as a recognition of the role urban planners could play to forge policies towards more sustainable food systems.Guerrero SFB (2019) Sustainable urban food districts (SUFD): Strategical spatial planning in urban food systems.An analysis to the Toronto food strategy policy
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".