285 The sharing economy: hazards of being an uber driver
Bibliographic record
Abstract
Introduction Uber ride-sharing is an important sharing economy challenge. The taxi industry is notoriously dangerous; even regulated and licensed professional drivers face a homicide rate higher than police officers and first responders. Uber drivers lack special licenses, organised workplaces and other usual safety structures. However, Uber touts different safety features, including feedback and ratings. Our study is focused on understanding the day-to-day work conditions and risks of Uber drivers. Method We conducted a critical interpretive study of ride sharing with Uber drivers, passengers and management, taxi managers and related policy makers in Ontario, Canada. Data include interviews and focus groups with 50 drivers, passengers, taxi and Uber managers and key informants. These were recorded verbatim, coded and analysed using strategies of coding, indexing and charting in a framework analysis. Results Uber drivers face unique risks relating to insurance coverage, the driver rating system, financial incentives, and asymmetric information. Drivers were not disclosing their ride sharing activity to their personal auto insurers in order to avoid premium hikes, thereby risking fraud charges and insurance cancellation. The Uber driver rating system prompted drivers to tolerate difficult and hazardous rides to avoid the risk of low ratings by passengers. Drivers received incentives to drive even when they were trying to sign off. Finally, passenger destination was blinded to the driver, creating asymmetric information conditions that limited driver choice of ride destination and duration. Conclusion Many of the driver risks related to drivers’ own precarious economic situations and need for income together with pressures from the Uber app to engage in unsafe working conditions. Although Uber drivers are touted as only ‘sharing’ a ride and as their ‘own boss’, in practice their driving activities were strongly governed by the Uber app
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".