Recycling Intention and Behavior among Low-Income Households
Bibliographic record
Abstract
To improve our knowledge of how to protect the environment, this study examined the factors that influence recycling intention and behavior among low-income households. The study adopted a cross-sectional design that relied on 380 low-income households who live in coastal Peninsular Malaysia. The findings revealed a positive effect of eco-literacy, environmental concern, and self-efficacy on the attitude towards environmentally friendly products. Subsequently, the findings also illustrated a positive effect of normative beliefs on subjective norms. Moreover, the results revealed a positive effect of attitude towards environmentally friendly products and perceived behavioral control (PBC) on recycling intention. Finally, there was a positive effect of both PBC and recycling intention on recycling behavior. Although this study’s focus on a specific income group from a single country could limit generalizability; the findings nevertheless provide scholars and policymakers with significant insights into promoting recycling activities, which are expected to contribute to the environment and reduce the environmental and economic vulnerability among low-income households. Therefore, environmental and socio-economic development organizations should assess the feasibility of recycling materials and develop a supportive system that facilitates and encourages recycling activities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".