Co-construction as Implementation: The Circular Economy Experience in Quebec—Consolidation Stage (2021–2024)
Bibliographic record
Abstract
Abstract The chapter explores Quebec’s circular economy experience, documenting the roles of academia, government, organizations, and businesses between 2021 and 2024. Its four main messages are the following. First, Quebec has established several key research infrastructures which encompass numerous sectors and interdisciplinary collaborations, positioning Québec as a leader in North America’s circular economy research landscape. Those research networks are interconnected and Quebec’s focus on circularity also intersects with climate change and biodiversity conservation efforts. Second, Quebec’s research endeavors have impacted policies related to the circular economy by influencing legislation, fostering industry partnerships, and supporting regional strategies. Third, Québec has successfully developed a common language around circular economy concepts, aided by a structured definition, visual aids, and an official lexicon. Fourth, Québec views the circular economy not merely as an operational model but as a societal transition project, aligning with broader sustainability goals beyond production and consumption efficiencies. Despite these achievements, Québec’s circularity rate remains at 3.5%, indicating a substantial gap between theory and practice. While the circular economy offers pathways to sustainability, it may not sufficiently challenge broader economic growth and consumption-based models. Quebec’s research community should critically explore alternative perspectives like degrowth and sobriety to address these systemic challenges more comprehensively.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".