I drive to work, sometimes: Motility capital and mode flexibility among young adult gentrifiers
Bibliographic record
Abstract
This chapter contributes to research exploring young adults’ changing transportation patterns in the context of gentrification and youthification. Of particular interest in this chapter is the phenomenon of multi-modality in commuting behaviour, which the authors call mode flexibility (e.g. driving to work one day, biking the next and taking the bus for the remainder of the week versus using the same mode for all commuting trips), and how differences in mode flexibility (i.e. not being constrained to any one transportation mode for a given trip) may relate to structural inequalities arising from gentrification and the workings of housing markets. The authors examine this issue by conducting a statistical analysis of novel primary data from an online survey of almost 700 people aged 18 to 40 in the USA and Canada. The authors find that multi-modal behaviour is partly an outcome of higher income earners with cars moving into areas where other modes are also easily accessible. Further, non-family households without children are more likely to be flexible in terms of daily mode choice. The authors advise planners and policymakers to acknowledge the constraints to people’s mode flexibility and to ensure that transit models are more equitably distributed in urban areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".