Waste not, want not: planning for consumer goods reuse in municipal waste management strategies
Bibliographic record
Abstract
While sometimes buried beneath other urban discourse, the management of waste has long been, and remains, an important consideration for cities. Reuse as a means of waste prevention is well understood to be an integral component of municipal waste management strategies. Considering the breadth of issues important to contemporary planning, in addition to the profession’s focus on holistic, systems-level thinking, puts matters such as waste management, waste prevention, and reuse well within its scope. Within this paper, a case study methodology is utilized to investigate instances of municipally-driven consumer goods reuse. This paper highlights the distinctiveness of this practice and furthers the understanding of how this component of waste management is addressed in locales comparable to Toronto. Emerging from this research is the indication that an opportunity is present to expand municipal involvement with consumer goods reuse in Toronto, including through greater sector specific reporting and by considering further strategies for multi-family residential buildings. While this paper is largely framed through a waste management lens, the concerns of this sector can be seen to link strongly to urban planning considerations, such as sustainability, resilience, and the imperative to consider the needs of future generations. Key words: Reuse; Municipal Solid Waste Management; Consumer Goods; Toronto
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".