Waste Management & The Circular Economy in Canada: An Analysis of Policy Layering
Bibliographic record
Abstract
Waste has become intrinsic to everyday life, where the average person throws away packaging or products no longer needed on a regular basis and does not know much about the rest of the item’s life cycle or where it came from originally. However, waste management is increasingly becoming one of the most challenging responsibilities of jurisdictions around the world. With the costs of maintaining operable waste management systems, such as landfilling and recycling, rising at the same time as environmental and socio-economic pressures, innovative solutions are needed. An answer that is becoming increasingly popular is the circular economy, which closes the loop of the linear business model by minimizing the input of new, raw materials and resources. This is achieved through designing products for reducing, reusing, or recycling as much as possible instead of jumping to the traditional ways of waste management. While many countries, industries, and advocacy organizations have already implemented some circular policies, little is known about an optimal design. Much of the literature speaks of the need for a paradigm shift to achieve a circular economy. Given the well known difficulties of bringing about such a shift, I investigated Canadian provincial policy instruments used to generate the circular economy to discover whether incremental first and second order policy changes are bringing about policy designs that promote circularity. Focusing primarily on the provinces of Saskatchewan (Western region), Ontario (Eastern region), and Nova Scotia (Atlantic region) in Canada, I have evaluated the shift from waste management to waste reduction to circular economy using the full spectrum of policy changes from patching to packaging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".