Urban agricultural practices and initiatives in built environments: case studies of Detroit and Singapore explored
Bibliographic record
Abstract
Global communities are recognising the importance of integrating urban food production locally and adopting to agrarian lifestyles in cities. In this paper, review and analysis on three selected world cities: City of Vancouver, New York, and Hong Kong are conducted considering important factors of urban characteristics and practices, initiatives and performances of urban agriculture and a set of criteria is formulated. A comparative analysis of two case studies of different density cities: Detroit and Singapore is undertaken in detail based on this criteria developed. Evolving out of varying contexts and processes that have shaped urban agricultural movements in these cities, this research offers a unique insight into the lives of these two cities. Urban agriculture plays vital roles in world cities in sustaining and creating liveable and productive places and building community resilience. Outcomes suggest that functional processes and appropriate and well-aligned policies and strategies on urban food production linked to urban planning policies can transform cities and can create curative places for wellbeing and improved food security of residents. Involvements of government, private organisations, local councils, and residents would be essential for a successful long-term continuation of these practices. Future research should focus to strengthen transdisciplinary connections between health, planning and other disciplines.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".