Bibliographic record
Abstract
At its core, waste management is a sustainable development struggle, which, if treated improperly, poses severe consequences to human and environmental health. This paper will unpack the socio-economic, cultural, and ecological implications of solid waste management, and explore the potential solutions to alleviating the burdens of improper disposal and treatment of waste on different scales. In order to achieve the United Nations Sustainable Development Goals (UN SDGs)– particularly the targets for Responsible Production and Consumption (SDG 12) and Sustainable Cities and Communities (SDG 11) – we must not view waste management in silos. Rather, we must encourage responsible behaviors and regulation from the local, regional, national, and global scales, with particular emphasis on the obligations of affluent systems and the capacity building of under-developed systems to effectively mitigate the consequences of improper treatment and disposal of solid waste. `The exploration of this issue is inspired by the rollout of the City of Edmonton’s new waste management scheme involving the collection of separated waste carts, with a pilot project in 2019 and full launch of the Cart Rollout in spring 2021 (City of Edmonton, 2021b). It is a point of interest to now reflect on the impacts of this updated system, and how it has (hopefully) reduced landfill accumulation and improved the overall outlook for establishing successful local waste management. This paper will therefore address the following questions: How does the waste management approach in Edmonton interact with and encourage positive multi-scalar actions (i.e. a ‘trickle- up’ effect)? How does each succeeding scale (regional, national, and global) influence city-level waste management (i.e. a ‘trickle-down’ effect)? Finally, what insights does this provide about sustainable solid waste management as a whole?
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| 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".