Exploring the Role of Knowledge in Social Acceptance of ELV Policy in Malaysia
Bibliographic record
Abstract
In developing countries where comprehensive policies addressing the environmental impact of ELV have been implemented, this mixed-methods study examines the complex relationship between knowledge and social acceptance of ELV policies in developing countries.The study integrates a quantitative survey with 150 participants and a qualitative phase featuring indepth interviews with 15 individuals.The quantitative survey explores participants' understanding and acceptance of various aspects of ELV policies, revealing diverse knowledge levels on environmental, economic, public health, safety, and technological dimensions.Notably, there is a solid willingness to comply with these policies, highlighting their perceived importance in safeguarding environmental and public health.The qualitative phase delves deeper, uncovering factors influencing social acceptance, such as limited awareness, positive attitudes, and considerations related to economic, safety, and health concerns.This study emphasizes the critical role of knowledge in shaping the social acceptance of ELV policies, demonstrating that an informed public is more inclined to have favorable attitudes and greater acceptance.By blending quantitative and qualitative insights, we obtain a holistic understanding of the interplay between knowledge and social acceptance concerning ELV policies.This comprehensive perspective is invaluable for policymakers and stakeholders, underscoring the necessity of well-informed strategies to boost public comprehension and acceptance of ELV policies.The findings indicate that effective communication and education initiatives could significantly enhance the implementation and effectiveness of ELV policies in developed nations, suggesting a pivotal role for targeted educational and awareness campaigns in achieving policy goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".