Intensity of Citizens’ Perception and Behavior Towards Electronic Waste Management (A Case Study, Qom city)
Bibliographic record
Abstract
Background: The growing use of electrical and electronic equipment in recent decades has generated a noticeable volume of electronic waste (e-waste), posing a major global environmental challenge. Therefore, the current study investigated household electronic waste management behaviors among citizens of Qom province, Iran. Methods: The data for this cross-sectional analytical study were collected via a validated questionnaire from 384 citizens visiting recycling centers in Qom. Subsequent analysis was performed using Excel and SPSS software. Results: The was found that only 37% of the respondents perceived the severe environmental consequences of e-waste. In contrast, the majority (93%) were aware of the direct economic benefits of recycling, while 41% recognized the indirect economic benefits. Regarding management behaviors, repair and reuse were the most common strategies (reported by 45% to 94% of the respondents), whereas delivering waste to official recycling centers was the least common (less than 5%) Conclusion: A lack of awareness regarding the environmental consequences of e-waste reduces its separation rate. Thus, decision-makers should incorporate the economic incentives that promote the sale and reuse of electronic equipment into e-waste management plans.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".