Developing a Circular Building Materials System and Fostering Innovation from Construction, Demolition and Renovation (CRD) Waste: An Ontario-focused Systemic Policy Analysis and Blueprint for Change
Bibliographic record
Abstract
The built environment in Ontario contains a diverse array of structures, spaces, and infrastructure systems designed to support and enhance the lives of its residents. But structures also create waste. Lots of waste. This paper is about that. \nThis research examines why we have so much waste and explores what can be done about it. It investigates a comprehensive understanding of the challenges and opportunities for innovative, underused, or cross-sectoral Ontario provincial policy options that can foster the circular use of waste and grow the circular built environment. \nIt combines a literature review of global and local practices, semi-structured interviews with stakeholders across various sectors, and a systemic analysis of provincial waste management policies. Additionally, it leverages information from a participatory design workshop with government officials and industry professionals that utilized generative design and foresight tools to develop innovative policy solutions. \nFinally, this paper aims to be solution-oriented and innovative but grounded in today's policy conversation. It aims to support policy-makers and decision-makers by offering a series of policy interventions to transform Ontario's waste management and development practices towards a more circular system.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".