Just mobility in the city: the case of non-motorized households in car-dependent cities
Bibliographic record
Abstract
By the simple fact of not owning a private automobile, some households are already experiencing sustainable mobility. They rely on public transportation networks, cycling and car sharing and tend to travel less than car-owning households. However, current sustainable mobility policies often do not take into account these kinds of households, focussing instead on convincing car owners to shift in their modal behaviours. We examine the mobility behaviours of non-motorized households living in several car-dependent Western Cities and consider ways to make public policies more just towards them. We consider this group as an everyday example of sustainable mobility and examine policy solutions that could make it easier for those foregoing private car ownership. We compare the similarities and differences between North American and European households by surveying households in Quebec City (Canada) and Strasbourg (France). In total we interviewed 57 households to describe their mobility behaviour and find out from them what policies are missing. We also inquired about social exclusion associated with their lack of private vehicles in a car-dependent society. In this paper we present our initial findings regarding what our interviewees suggest to improve public policy concerning their sustainable mobility. We also expose their ideas of a perfect world for non- motorized households.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".