Public Knowledge Regarding E-Waste Recycling in Kuala Lumpur, Malaysia
Bibliographic record
Abstract
E-waste is one of the major global environmental issues, defined as electrical and electronic appliances that are no longer used or eventually break down and are discarded. Generation of E-waste over time has become a red alarm as it can impact the environment and human health negatively. One key approach towards sustainable management of E-waste is through E-waste recycling, where the base of action is with the public itself. In order to create the necessary awareness of the problem, and to design and implement such sustainable practices, knowledge is one of the key elements that can assist in minimizing the deleterious effects of such global environmental issues. This cross-sectional study was conducted among 543 respondents in Kuala Lumpur, the capital city of Malaysia, and aimed to determine knowledge levels in the broader public regarding E-waste recycling, based on the demographic background of the respondents. Five out of eight demographic variables with the p-value of <.05, are namely: gender, age, marital status, occupation and residential type. It means relevant stakeholder such as governmental authorities, NGOs and private sectors also play important roles in providing the public with knowledge related to E-waste recycling. Knowledge can be delivered through different channels of information sources based on the suitability of the receiver, in connection with their demographic background.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".