Car dependency in the urban margins: the influence of perceived accessibility on mode choice
Bibliographic record
Abstract
Car dependence is a dimension of transport poverty whose subjective components have been limitedly explored. Research on car dependence highlights the incidence of transport costs on family budgets, assesses the multidimensional vulnerability of car-dependent subjects and the possibility to access valued opportunities. However, people’s perceptions and their perceived ability to access relevant places can also influence the way in which they move in car dependent settings. In this paper, we aim to examine to which extent perceived accessibility influences mode choices in such areas. Based on a survey carried out in four peripheral and periurban municipalities in the Metropolitan Region of Santiago de Chile, we examine how subjective perceptions of accessibility contribute to explain modal choice in the outskirts of Santiago. Results show that perceived accessibility has a negative net impact on the utilities for both car and public transport, which means that a low perceived accessibility increases the likelihood of choosing motorized modes. Moreover, residents from peripheral municipalities tend to perceive a higher accessibility than households from periurban areas, which are excluded from the public transport system. These findings show the importance of providing nearby opportunities and convenient alternatives to the car in order to limit car dependency, especially in periurban areas.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".