Expert yet vulnerable: Understanding the needs of transit dependent riders to inform policy and design
Bibliographic record
Abstract
Transportation is a crucial resource that links people to jobs, social networks, community and services. The transit dependent -- those who do not own private vehicles -- occupy a unique position. They are expert in their knowledge of public transportation while vulnerable to the failures and limitations of transit. This paper presents the results of a study that is aimed at understanding the lived experience of transit dependent riders. Using a framework of structuration theory as an analytic lens, we provide a thematic analysis of qualitative data including interviews with socially connected groups of people and video diaries. The results demonstrate the expertise that transit dependent riders have about transit and its policies and how they deploy that expertise in productive and cunning ways to make the system work for them. The analysis of this data resulted in three categories of agency to consider when designing for vulnerable populations: resourcefulness, reciprocity and powerlessness. The paper concludes by advocating for a human-centered approach to designing systems in community informatics and offers a set of guiding questions for designers of information and communication technologies (ICTs) to consider, especially with regards to vulnerable populations.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".