Navigating the Shift: Strategies Beyond “Build It and They Will Come” for Sustainable Mobility in Quebec
Bibliographic record
Abstract
Abstract Passenger transport is an important contributor to unsustainable urban systems. To achieve the necessary socio-ecological transition will require overcoming the entrenched system of automobility. Composed of several mutually reinforcing components, this system has conferred psychosocial dimensions to car ownership and use that leads to important institutional, political, and individual resistance to change both car-centric transportation infrastructure and individual travel behaviour. For this reason, a growing consensus suggests that transitioning to a sustainable mobility system requires a more holistic approach that applies a synergistic integration of “hard” supply-side measures and “soft” demand-side solutions. This means increasing non-automobile accessibility and supporting such change with soft travel behaviour change solutions that target social-psychological barriers to change. While such approaches have demonstrated their effectiveness around the world, this second category of interventions remains underutilized, particularly in North America. Drawing from social psychology and a North American case study, this chapter proposes a theory-to-practice guide for practitioners to designing effective voluntary travel behaviour change interventions based on the Stage Model of Self-Regulated Behaviour Change (SSBC). A four-level integration framework for intervention design based on the SSBC is proposed. The framework proposes intervention approaches from using the model as a simple diagnostic tool to a complete integration to deliver a fully individualized and stage-tailored intervention. Stage-specific messages and strategies are described to shift people away from car use towards active, collective, and shared mobility options. The chapter concludes on suggestions for collaborative efforts between researchers and practitioners to design, evaluate, and enhance the effectiveness of these interventions, thus moving beyond infrastructure-only solutions to foster a successful transition to sustainable mobility in Québec.
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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 | 0.001 | 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".