Moving Towards Mobility Justice: Challenges and Considerations for Supporting Advocacy
Bibliographic record
Abstract
In response to climate change and continued urbanization, urban transportation systems around the world are undergoing transitions to promote lower-emission vehicles, public transit, biking and walking. However, mobility is a complex issue that raises important questions of social justice as a result of its connections to numerous aspects of everyday life and the broader social and political contexts. Drawing on interviews with transportation advocates across Canada, we identify four ways in which the design and use of existing mobility tools and technologies perpetuate mobility injustices, and deepen the divide between urban planners and the public. Looking across these arguments, we note path-dependence in transportation knowledge infrastructures as a common barrier to mobility justice advocacy that can be difficult to recognize or overcome. Finally, we consider tactics that research in HCI and CSCW might pursue as part of efforts to unsettle path-dependence and reorient transportation planning towards mobility justice.
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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 | 0.155 | 0.202 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.052 | 0.071 |
| Scholarly communication | 0.063 | 0.056 |
| Open science | 0.012 | 0.054 |
| Research integrity | 0.038 | 0.034 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".