Exploring mobility of care with measures of accessibility
Bibliographic record
Abstract
Accessibility, the ease of interacting with potential opportunities, is an increasingly important tool among transport planners aiming to foster equitable and sustainable cities. However, in accessibility research there is a historical focus on employment destinations that is shaped by a masculinist transportation planning tradition. This paper aims to counter this gendered bias by connecting the Mobility of Care framework, a gender-aware transport planning conceptualisation to an empirical accessibility analysis of care destinations in the City of Hamilton, Canada. Care destinations are all the places one must visit to sustain household needs such shopping, errands, and caring for others. This paper considers access to care across different modes of transport at two travel time thresholds (trips shorter than 15-min and 30-min) using a curated care destination dataset. The accessibility methods used includes the cumulative opportunities measure and a competitive and singly-constrained accessibility measure (spatial availability) for different modes. Overall, results indicate that accessibility by car is exceptionally high across the city, while access by public transit, cycling and foot is relatively low with some exceptions in the inner city. Notably, there are distinctions between both methods: cumulative opportunities illustrates a more optimistic potential interaction landscape for non-car modes, while the spatial availability measure demonstrates a theoretically more realistic spatial distribution of care destination availability of potential interaction. Neighbourhoods with both low spatial availability to care and a high proportion of low-income households are also identified and discussed as areas in need of intervention. The manuscript and analysis is computationally reproducible and openly available. The presented analysis demonstrates methods planners can use to apply a gender-aware lens to accessibility analysis. Further, results can inform policies aiming to encourage sustainable mobility.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".