A Study of E-Waste Management Programs: a Comparative Analysis of Switzerland and Ontario
Bibliographic record
Abstract
Electronic waste (e-waste) is being generated around the globe at a high rate. High market penetration of electrical and electronic equipment (EEE) and the fast development of more innovative designs by producers and manufacturers on a regular basis make the current electrical and electronic equipment obsolete faster than before, which contributes towards the generation of more e-waste. To combat the issue, e-waste management programs are being developed, implemented, or evaluated in many jurisdictions around the world. Ontario is one of the jurisdictions that have taken initiatives and implemented an e-waste management program to address the rising quantity of e-waste. This thesis evaluates the Ontario's e-waste management program by using Logical Framework Approach (LFA) as an evaluation framework, and focusing on the criteria for a normative e-waste management program. It utilizes the Swiss e-waste management program as a case study to provide a comparative analysis, and extract valuable lessons through the application of the lesson-drawing approach that can be applied to improve the effectiveness of the implemented e-waste management program in Ontario.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".