What shapes perceived accessibility? Unpacking the interplay of sociodemographics, latent perceptions, and traditional accessibility metrics
Bibliographic record
Abstract
Accessibility, understood as how easily individuals can reach a desired destination, can be measured in multiple ways. Perceived accessible opportunities, which include those seen as viable alternatives and as feasible given personal and scheduling constraints, may vary from an analyst’s assessment when calculated accessibility measures are used. Our study explores how perceived accessibility is shaped by the interaction between sociodemographic attributes, latent perceptions of the built environment, and traditional accessibility indicators. Moreover, we investigate how accessibility indicators influence perceived accessibility for various services and examine the effects of distance thresholds to determine how strongly the concept of the neighbourhood influences residents’ perceptions of accessibility. To do so, we use a representative sample of 354 individuals living in a residential neighbourhood in Santiago de Chile in 2019. In addition to mobility diaries and sociodemographic characterisation, the respondents indicated whether eight different services were accessible in the neighbourhood and, if so, whether they made use of them. A hybrid discrete choice model was estimated to understand the interplay between different factors and their role in the perception of accessibility for each service. Our results indicate that even though each is important on its own, there is no evidence suggesting that one accessibility indicator is superior to another when studying accessibility perception. In addition, we find both direct and indirect effects of gender and age, which occur via latent perceptions of the use of public space and infrastructure quality. Finally, we found evidence supporting the idea that the neighbourhood corresponds to the immediate surroundings of the residential household and that perceived accessibility is significantly different depending on the type of service studied. This study contributes to a deeper understanding of the underlying dimensions affecting perceived accessibility in the Global South, providing important evidence for decision makers on the complexities that must be considered when designing effective and inclusive proximity-based policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".