Algorithmic control and gig workers: a legitimacy perspective of Uber drivers
Why this work is in the frame
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Bibliographic record
Abstract
Organisations increasingly rely on algorithms to exert automated managerial control over workers, referred to as algorithmic control (AC). The use of AC is already commonplace with platform-based work in the gig economy, where independent workers are paid for completing a given task (or “gig”). The combination of independent work alongside intensive managerial monitoring and guidance via AC raises questions about how gig workers perceive AC practices and judge their legitimacy, which could help explain critical worker behaviours such as turnover and non-compliance. Based on a three-dimensional conceptualisation of micro-level legitimacy tailored to the gig work context (autonomy, fairness, and privacy), we develop a research model that links workers’ perceptions of two predominant forms of AC (gatekeeping and guiding) to their legitimacy judgements and behavioural reactions. Using survey data from 621 Uber drivers, we find empirical support for the central role of micro-level legitimacy judgements in mediating the relationships between gig workers’ perceptions of different AC forms and their continuance intention and workaround use. Contrasting prior work, our study results show that workers do not perceive AC as a universally “bad thing” and that guiding AC is in fact positively related to micro-level legitimacy judgements. Theoretical and practical implications are discussed.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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