Labor issues from the perspective of drivers on the Uber and Lyft apps and the impact on riders who use wheelchairs
Bibliographic record
Abstract
• Qualitative investigation of perspectives of drivers on Uber and Lyft apps. • Drivers say extra time and work is required to transport wheelchair users. • Lack of compensation for extra time and work encourages service decline by drivers. • App technology can be used to track service declines to wheelchair users. • Using bonus payments can help discourage service declines by drivers. Wheelchair accessibility of transportation service hailed using Uber and Lyft is fraught with contention. In this research, I interview 12 drivers on the apps who work in Washington, DC to understand their experience and perception about issues surrounding service to wheelchair users. Some drivers experience transporting wheelchair users as markedly different from service to non-wheelchair users due to the uncompensated labor they perform when assisting wheelchair users and the additional time required. They perceive service decline by drivers to possibly stem from lack of compensation for their time and work. One solution to address the problem could be to use app-technology to keep a record of ride requests by wheelchair users who volunteer to disclose disability status and incentivize drivers for completed rides. The overarching purpose of the study is to create knowledge that can contribute to overcoming potential barriers to full inclusion of disabled riders in the app-hailed transportation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".