Bottom-up strategies, platform worker power and local action: Learning from ridehailing drivers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it