Analyzing the sustainability of electronic waste management in Toronto, Ontario
Bibliographic record
Abstract
Sustainable waste management plays a key role in achieving sustainable urban \ndevelopment worldwide. Currently, the rates of waste generation are on the increase with \nelectronic waste comprising a significant portion of the total. This growth in the \ngeneration of electronic waste has led to the creation of sustainable management \nprograms in a number of cities, including Toronto in Canada. An examination of the \nexisting electronic waste management system in Toronto, Ontario has provided many \ninsights into the structure of the relationships and the flow of the electronic waste within \nthe area. This thesis analyzes the sustainability of the social networks and material flow \nnetworks that have developed within the Toronto electronic waste market. The data, \ncollected from the field observations in the summer, points to a relatively uneven \ndistribution of partnerships between the large-scale recycling corporations, government \norganizations, non-profit refurbishers and the informal recyclers. The examination also \nreveals a prioritization towards large-scale mechanical recycling over refurbishing and reuse \nof the electronics. The effect of such distribution of material and partnerships on the \noverall sustainability of the management system is discussed.
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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 | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".