Creative counter-discourses to the “green city” narrative: practices of small-scale urban agriculture in Hanoi, Vietnam
Bibliographic record
Abstract
As a central component of the “green city” narrative, urban agriculture is gaining importance in urban planning and global sustainability agendas. In Hanoi, the capital city of Vietnam, the “green city” is core to the state’s urbanisation agenda, with a green corridor envisioned as part of the city’s Master Plan for 2030. We investigate the patterns and processes of small-scale urban agriculture underway in this green corridor to better understand whether this type of agriculture actually intersects with, and is supported by, state plans. We frame our paper in conceptual debates around food safety and everyday governance, while supporting our analysis with data from interviews with resident gardeners and officials, as well as the mapping of urban gardens in seven wards in and alongside the green corridor. We pay attention to practices and motivations of residents who maintain small-scale vegetable and fruit plots (the most prevalent form of urban agriculture), and the challenges and constraints they face. Our work reveals the temporary and interim status of urban agriculture in Hanoi, highlighting the contradictions within Vietnam’s “green city” discourse. Nonetheless, urban residents still undertake urban agriculture, negotiating or compromising with state officials, to meet their demands for fresh and safe food.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".