CITIZENS' ATTITUDE AND PERCEPTION TOWARD ROOFTOP GARDENING IN CITY AREA
Bibliographic record
Abstract
Bangladesh, well known for its abundant green resources, has experienced rapid population growth and uncontrolled infrastructural development in cities, resulting in a loss of urban green spaces and environmental problem. Rooftop gardening has been proposed as a sustainable and affordable solution to mitigate these problems and create green spaces of cities in Bangladesh. This study assesses the social acceptance of rooftop gardens among city dwellers in Bangladesh. A descriptive quantitative study was conducted using convenient sampling methods. A total of 475 participants, including half of them male and another half female, were surveyed through an online Google form questionnaire in eight divisional cities and district areas across Bangladesh. Data were analyzed using SPSS and MS Excel, including descriptive statistics such as percentage distribution and chi-square test and inferential statistics such as correlation coefficient. The study found that approximately three quarter of respondents had positive attitude scores towards rooftop gardening, while almost a third had negative attitude scores indicating unwillingness. In terms of behavioral perceptions, nearly three quarter of respondents had positive scores, while almost a third had negative scores. The study also found associations between attitude, behavior, and socio-demographic factors such as sex (p<0.05). Furthermore, a positive linear association was observed between respondents' attitudes and behavioral perceptions towards rooftop gardening, as determined by the correlation coefficient. However, the study revealed that one third of respondents still had negative attitudes and just over a third had negative behavioral perceptions towards rooftop gardening. Therefore, effective policy measures and joint initiatives from the government and city municipalities are necessary to enhance the acceptance of rooftop gardens among the respondents.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".