Variation in Public Perceptions Across Sustainable Transport Projects in Montréal, Canada
Bibliographic record
Abstract
Public opinion is one of the main drivers of political action in relation to sustainable-urban transitions.However, little research has been conducted to understand how the characteristics of different sustainable-transport projects influence public opinions.Drawing from both quantitative and qualitative data from Montréal's 2021 Mobility Survey, this paper analyzes three transport projects -a light-rail (LRT), a bus-rapid transit (BRT), and an express-cycling network (REV)-to evaluate the characteristics that contribute to positive and negative social perceptions.Quantitative statements pertaining to six different project impacts were summarized and compared between projects, showing statistically significant differences between the projects.Qualitative data was pulled from open-ended questions for each project and analyzed using thematic analysis.Negative perceptions associated with the LRT were related to aesthetics and governance issues, whereas perceived detrimental impacts on businesses were more commonly associated with the REV and the BRT.The BRT was found to be exemplary in governance due to the inclusive consultation, the REV was praised for its speed and construction in phases, and the LRT was praised for providing higher accessibility to individuals.The findings from this research can be of benefit to practitioners and policy makers as they shed light on the various characteristics that positively and negatively impact public perceptions of three different sustainable-transport projects.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".