Green Gardening Practices Among Urban Botanists: Using the Value-Belief-Norm Model
Bibliographic record
Abstract
Recently, the increase in urbanization has momentously intensified the climate issues in urban centres. A large number of the population is vulnerable to climate change living in urban areas all over the world. The decline in agricultural activities leads to food insecurity in urban areas. Urban gardening is promoted as a viable option to achieve food security and help reduce the climate impact in urban areas. Urban gardening can help reduce the carbon footprint, curtail food production’s time and distribution costs, and offer food security and safety. Urban agriculture can be categorized as sustainable as it has economic, social, and climate impacts. The value-belief-norm framework is utilized to evaluate green gardening intentions and practices. The online survey collected cross-sectional data from 1,721 urban respondents in Malaysia. Based on the data analysis performed with structural equation modelling partial least square regression (SEM-PLS), it was found that the biospheric, egoistic, and altruistic values significantly influenced the new environmental paradigm. The environmental paradigm, awareness of consequences, and ascription of responsibility have significant positive effects on personal norms to engage in green gardening. The green trust and personal norms promote green gardening intention, subsequently leading to green gardening practices. A community-level effort is required to mitigate the climate change issue caused by urbanization and address food availability problems. Civic administration and residents should work together to protect the green spaces in urban centres, which promotes public well-being.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".