Does Public Transport Planning Consider Mobility of Care? A Critical Policy Review of Toronto, Canada
Bibliographic record
Abstract
The concept ‘mobility of care’ captures all the daily travel necessary for the upkeep of a household, including trips to grocery stores, health-related appointments, errands, and caring activities for dependents. Since it was originally coined in 2009, a handful of studies have shown how poorly mobility of care trips are captured in transportation surveys. These preliminary analyses also find that care trips comprise a substantial proportion of daily mobility. As women disproportionately engage in ‘mobility of care’ travel, the under-consideration of care trips is argued to result in a gender bias in transport planning. Despite this, transport policy related to mobility of care has received less attention. Given that transport policy shapes how transport systems operate, this paper explores the extent to which recent transport policies consider mobility of care. A critical policy review framework is used to systematically examine seven policy documents (435 pages) from the Toronto Transit Commission (TTC), the largest transit agency in Canada. Results indicate that mobility of care is rarely directly considered or significantly discussed. Instead, transport policy often uses the commute to work as the default trip. Mentions of care destinations and trip characteristics associated with mobility of care are more common in recent years and most frequently discussed in relation to the COVID-19 pandemic or specialized services for seniors and people with disabilities. Policies that likely facilitate mobility of care indirectly are also identified, including fare discounts, transfer windows, and accessibility policies. The review concludes with preliminary recommendations on how transit agencies can more directly plan for mobility of care.
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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 | 0.032 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.018 | 0.025 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| 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".