Legitimacy, voice and power in ride hailing labour movements in Kenya
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
Abstract One way to improve gig work may be to strengthen worker voice via worker organisations. But organising gig workers under economic strain is difficult. In this article, I apply the power resources approach to two related but divergent cases of digital driver mobilisation—Mombasa and Nairobi—to demonstrate the relationship between the cultivation of internal and external legitimacy and the implications each has for different types of power. In Mombasa, association leaders mobilised a majority of local drivers and built internal legitimacy through geographic zone groups, direct communications and democratic norms. This legitimacy enabled them to use structural power, taking direct action against app companies. Unable to build internal legitimacy, Nairobi association leaders instead cultivated external legitimacy through media and politician appeals, using political power to press for regulation. However, workers contested representation by these leaders, raising questions about whether relying solely on external legitimacy increases effective worker voice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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