Bottom-up strategies, platform worker power and local action: Learning from ridehailing drivers
Bibliographic record
Abstract
In the digital gig economy, workers generally have limited power and are disadvantaged compared to platform operators, who are usually large technology firms. Workers are often independent contractors rather than employees in this emerging form of work. While beneficial to platform companies, these arrangements place considerable risk on workers. Moreover, the structure of the gig economy presents challenges to traditional labor organizing strategies. To identify strategies used by ridehailing drivers to improve their working conditions and highlight points of intervention for policy makers and labor organizers, we draw upon an analysis of interviews and videos posted by YouTube diarists working for Uber. We find that ridehailing drivers improve their working conditions through business planning, leveraging competition between platforms, building solidarity through social media, and using technology to manage the workplace. We find that drivers favor individualistic strategies and often lack the institutional support and knowledge to benefit more fully from these strategies. We argue that local governments and labor market intermediaries offer the potential to empower ridehailing drivers and reinvigorate interest in collective action through workforce development tools if they build on the strategies these gig workers already use.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".