Understanding support for cycling infrastructure through Moral Foundations Theory
Bibliographic record
Abstract
Cycling improves public health, is more space efficient than cars, and has very limited emissions. For these reasons, many cities are implementing cycling infrastructure to support people of all ages and abilities to benefit from this mode of transport. However, cities often face opposition, or “bikelash”. Since people's decisions and opinions on policies are often shaped by their morals, this study explores how morals, political beliefs, and personal characteristics influence support for cycling as a mode of transport. Using Moral Foundations Theory, which identifies six key morals that guide people's opinions: care (concern for others' well-being), fairness (justice and equality), loyalty (commitment to one’s group), authority (respect for leadership and tradition), purity (emphasis on cleanliness and self-discipline), and liberty (personal freedom and opposition to government intervention). An online survey of 1,606 residents in the Montréal Metropolitan Area assessed these morals using the 30-item Moral Foundations Questionnaire (MFQ-30) and additional questions to assess the liberty moral, alongside political beliefs, transport behaviours, and support for cycling. Linear regression analysis found that right-leaning individuals showed lower support for cycling, with liberty (opposition to government intervention) and authority negatively associated with cycling endorsement. Loyalty, typically linked to conservative morals, was positively correlated with support, while purity, care, and fairness showed no relationship. Car ownership was associated with reduced support, whereas environmentally conscious individuals and those interested in bikeshare programs showed increased support. The findings highlight the role of morals and political identity in shaping attitudes, offering insights for policymakers seeking to address opposition and broaden public support for health promoting infrastructure. • Moral foundations are useful predictors of support for cycling • Left-leaning individuals are more supportive of cycling as a mode of transport • Authority and government liberty are negatively associated with support • Loyalty is positively associated with support for cycling • People who cycle more are more likely to support cycling
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".