E-Waste Hazard and its Effect on Purchasing and Disposal Habit in Kaduna South Local Government Area, Kaduna State
Bibliographic record
Abstract
<em>This study examined Public Awareness Regarding E-Waste Hazard and its Effect on Purchasing and Disposal Habit in Kaduna South Local Government Area of Kaduna State. The researchers assessed the aspect of the e-waste situation by distributing questionnaire to families, formal & informal e-waste collectors, manufacturers, dealers, consumers and government officials in Kaduna South LGA, a large part of the city of Kaduna Metropolis. Insight from various respondents and workers were also sought. It was found that most respondents do not participate in formal e-waste recycling systems, are not aware of any specific details about the health and environmental hazards of e-waste, and do not know about any e-waste Act or legislation. Fifty-two (52.5%) of the respondents believe that the knowledge of e-waste hazard definitely will influence their attitude toward disposal of e-waste. Fifty-eight (58.5%) of the respondents are not aware of e-waste hazard on health and environment. While 66.5% of the respondents are not aware of any policy/ regulation on e-waste including the 16.5% that are not sure or undecided about knowing any e-waste legislation in the state. Additionally, only about one quarter have the knowledge of the possibility of reusability of used electronics. Majority of the respondents purchased electronic products due to the desire for new technology and need for greater functionality. However, they lack direct contact to dispose the older and damaged electronics for recycling or economic reward once the electronics are damaged or obsolete. The study recommends among others that </em><em>Government at all levels should negotiate with the private sector including the electrical electronic equipment manufacturers towards the creation of e-waste collection points across the nation to recover damaged and end-of-life e-waste from consumers, with some monetary reward (incentives) attached to it. </em>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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 teacher head, 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".