Quantification of E-waste collection count at household level
Bibliographic record
Abstract
Electronic waste (e-waste) refers to discarded electrical and electronic equipment that is unwanted as has reached an end of service life, no longer wanted, or obsolete. Existing literature shows a notable knowledge gap in e-waste assessment at household level. This study examines e-waste management of four e-waste categories (computers, televisions, cellphones, and audio-visual equipment) across three Canadian provinces of British Columbia, Quebec, and Saskatchewan. The number of devices at household level were first estimated from national data. The results showed that unlike other e-wastes, cellphones experienced an overall increasing trend, from 18 to 23 units per thousand people over the study period. In terms of household participation, British Columbia generally had a higher household-participation rate, possibly due to the earliest adaptation of Extended Producer Responsibility management framework in 2007. The findings of provincial comparison recommend a target program to promote recycling of obsolete cellphones in Saskatchewan. The likelihood of households properly recycling their unwanted electronic devices showed positive correlations with the economic indicators. For instance, average income is associated with households’ likelihood of recycling computers (+0.80, p < 0.001), televisions (+0.80, p < 0.001), and audio-visual equipment (+0.73, p < 0.001). The use of household data will pave the way for decision makers to design residential e-waste collection programs. ⁃ Weight based residential e-waste collection (kg/cap) has been decreasing in Canada ⁃ E-waste collection rate (unit/1000 people) at household level is estimated ⁃ Cellphone collection increased from 18 to 23 units per 1000 people in Canada ⁃ Computer donation program may be a barrier to residential e-waste program ⁃ Households' decision to dispose appears sensitive to Consumer Price Index
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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 | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".