Chemins de Transition: An Innovative Method of Knowledge Mobilization to Accelerate the Socio-Ecological Transition in Quebec
Bibliographic record
Abstract
Abstract This chapter explores “Chemins de transition,” an initiative that leverages its position within a knowledge institution to expedite socio-ecological transitions. Recognizing the potential of universities to contribute beyond their traditional roles, the project addresses pressing ecological challenges such as biodiversity loss, resource scarcity, and climate change. The chapter details the initiative’s origins, theoretical foundations, and a foresight-based methodology designed to mobilize over a thousand experts and stakeholders. This approach aims to develop new strategic planning tools for anticipatory governance and adapt them to societal needs. The project’s unique method combines academic, professional, and experiential knowledge to create a concrete narrative for transformation. Key aspects include the development of a “transition arena,” scenario planning, and participatory workshops that collectively envision desirable futures and outline trajectories to achieve them. The chapter also discusses the project’s learning outcomes, challenges, and success factors, emphasizing the critical role of participatory foresight in driving systemic societal change. This innovative approach has informed, trained, and engaged thousands of individuals and organizations across Quebec, demonstrating the importance of interdisciplinary collaboration and long-term strategic planning in achieving sustainable socio-ecological transitions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".