‘Don't take a poo!': Worker misbehaviour in on‐demand ride‐hail carpooling
Bibliographic record
Abstract
Workers actively negotiate contradictions between discourses of flexibility and entrepreneurialism and actually existing conditions of risk and precarity endemic to online self‐employed work. This article examines how ride‐hail drivers counter‐branded UberPool—a carpool ride‐hail service—as ‘UberPoo’. While marketed as a solution to congestion, UberPool created risky and coercive working conditions for ride‐hail drivers. Our analysis is from a study on ride‐hail driver experiences of health and safety risks in a large Canadian city. We engage the concept of organisational misbehaviour to explore how drivers mocked and avoided carpool rides despite the threat of penalties. We characterise misbehaviour as a struggle over lack of control and lack of autonomy in self‐employed work, providing evidence that despite their structural powerlessness, some ride‐hail drivers do set limits around the work they are willing to accept. Algorithmic management and ambiguously classified ride‐hail work are thus subject to some degree of subversion.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".