Electronic Waste Management in Canada
Bibliographic record
Abstract
Waste management is becoming a major global policy issue for several reasons including pollution control, the increasing demand for the extraction of rare and finite natural resources required for consumer and industrial product and the diminishing availability of landfill sites for disposal. Improper waste management and landfill use have implications for policy development regarding environmental degradation and human health issues. Of particular note is the increasing production and consumption of consumer electronic products, such as laptops, televisions and mobile phones, which are composed of rare and precious metals, other metals, plastics, minerals and chemicals. Many of these substances are toxic when inadequately disposed of or dismantled and they pose significant risks to human health and well-‐being as well as environmental damage through contamination of air, soil and groundwater sources. In addition, the improper disposal of end-‐of-‐life electronics and their components represents a significant resource loss. Improperly managing electronic waste is a global as well as a Canadian issue. A worldwide phenomenon of exporting electronic waste (e-‐waste) from the developed to developing world, often illegally and without adequate regulation, has been recognized over the past couple decades. The international community has stepped in to alleviate this ethical problem by prohibiting the export of hazardous waste from OECD to non-‐OECD countries. However, this international action has done little to encourage effective domestic waste management in Canada. Instead, provinces have taken initiatives at different levels, leading to misaligned policies at the national level. This purpose in this paper is to investigate Canada’s initiatives in terms of e-‐waste management and whether a more domestically focused strategy would be more effective in improving Canada’s landfill use, general waste management and environmental performance, as well as further deterring the illegal export of e-‐waste.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".