How to Expedite the Process of Circular Built Environment in Toronto: A Systemic Approach
Bibliographic record
Abstract
This research addresses the pressing need to reduce global emissions by 55% by 2030, as determined by recent UN evaluations, in response to the rapid temperature increase over the past five decades. The Paris Agreement was ratified with the aim of reducing global warming to below 2°C over pre-industrial levels. Toronto is striving to become a circular city through the implementation of circular economy strategies and initiatives. The aim of this research project is to gain a deeper understanding of the current state of the circular-built environment in Toronto and identify the barriers and enablers to transitioning towards circular practices. The study identifies the primary stakeholders in the built environment using the Actors Map and describes their roles and transformations, highlighting the obstacles and challenges to circularity as well as mapping them against circular strategies, approaches, and best practices. The researcher also maps circular strategies against the key stakeholders, identifies trends using STEEP-V analysis and the Three Horizons (3H) approach to examine the relationships between changes and innovations required to reach desired future outcomes. The study reveals gaps in the existing system and recommends the emergence of an abundance of trends that cover the entire range of circular practices, including those that have not yet been addressed, to bridge the divide between Toronto's circular vision and current progress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".