Situating divergent perceptions of a rapid-cycling network in Montréal, Canada
Bibliographic record
Abstract
As cities work to accelerate sustainable-transport transitions, the expansion of cycling networks has become a significant topic of debate. Even as cycling mode shares are increasing across a number of North American contexts, ‘bikelash’ (i.e. community opposition to cycling facilities) remains prevalent. In this paper, we draw from qualitative questionnaire data and spatial analysis from Montréal, Québec to contribute a situated understanding of factors influencing both positive and negative social perceptions of cycling infrastructure. Our analysis confirms general trends that contribute to residents’ overall satisfaction with recent cycling interventions, including enhanced safety considerations and family-friendly infrastructure. We also identify particular sources of bikelash that require deeper consideration, including conflicting ideas about the impacts of cycling facilities on local businesses, divergent opinions about the planning process, perceived inequities in the distribution of cycling networks, as well as issues of seasonality and modal integration. These findings can be of interest to practitioners and decision makers working to support sustainable-mobility transitions, including recommendations on public communication and consultation processes, winter cycling facilities, integrated infrastructure for active travel, as well as the inclusion of social equity and critical disability perspectives.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".