Postcapitalist precarious work and those in the ‘drivers’ seat: Exploring the motivations and lived experiences of Uber drivers in Canada
Bibliographic record
Abstract
In this inductive, qualitative study, we observe how Uber, a company often hailed as being the poster-child of the sharing economy facilitated through a digital platform may also at times represent and reinforce postcapitalist hyper-exploitation. Drawing on the motivations and lived experiences of 31 Uber drivers in Toronto, Canada, we provide insights into three groups of Uber drivers: (1) those that are driving part-time to earn extra money in conjunction with studying or doing other jobs, (2) those that are unemployed and for whom driving for Uber is the only source of income, and (3) professional drivers, who are trying to keep pace with the durable digital landscape and competitive marketplace. We emphasize the ways in which each driver group simultaneously acknowledges and rejects their own precarious employment by distancing techniques such as minimizing the risks and accentuating the advantages of the driver role. We relate these findings to a broader discussion about how driving for Uber fuels the traditional capitalist narrative that working hard and having a dream will lead to advancement, security and success. We conclude by discussing other alternative economies within the sharing economy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.030 | 0.016 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".