Stakeholder Perception and Institutional Approach to Rooftop Gardening (RTG) of Urban Areas in Dhaka, Bangladesh
Bibliographic record
Abstract
Dhaka is one of the world's most populated cities and lacks the open fields and greeneries required for healthy living. Whereas urban sustainability depends on the greenness of the environment and the reduction of food dependency on the rural supply, currently, the city is failing to meet both requirements. The aim of this research is to understand why rooftop gardening (RTG) has failed to find its place in the policy agenda of Bangladesh, despite having support from citizens and experts. In answering this question, the authors have analysed the current state of rooftop gardening in Bangladesh with its major challenges and opportunities, explored the perception held by the city residents and agriculture professionals toward rooftop gardening, and discussed the existing institutional structure affecting the current rooftop gardening practices. The research presented in this paper argues that even though citizens have a positive attitude about rooftop gardening and experts consider it a viable opportunity, the existing policy process has hindered it from being a part of the policy agenda. Two online surveys were conducted on city residents and agriculture professionals from government and academic institutions. Interested participants from both categories participated in online interviews for in-depth discussions. In effect, the study shows that in the case of Dhaka city, the issue-framing is still at a very early stage. Though experts understand the value of RTG, they have not managed to raise enough awareness. From this perspective, experts have not played an adequate 'instrumental' role. As these two streams are quite weak, they are unlikely to join forces and intersect with the politics stream, where no attention to Rooftop gardening (RTG) can be observed at this point. Consequently, the researchers have not seen the RTG issue achieve enough policy momentum.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".