Algorithmic Controls and their Implications for Gig Worker Well-being and Behavior
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
This study examines how the use of algorithmic controls embedded in gig economy platforms impacts worker well-being and behavior. We draw on the information systems (IS) control and technostress literatures to explore how different modes of algorithmic control correspond with (positive) challenge technostressors and (negative) hindrance technostressors experienced by gig workers. We also consider the technostress outcomes, in terms of continuance intentions and workaround use. Using a survey of 621 US-based Uber drivers, we find that algorithmic input controls positively relate to hindrance technostressors, but that algorithmic behavior and output controls positively relate to challenge technostressors. The study bridges the IS control and technostress literatures by conceptualizing algorithmic control modes as work demands that put gig workers under stress. This stress can have important downstream effects on worker behavior, which can impact the overall gig economy platform in the event that workers discontinue their work or increase their workaround use.
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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 | 0.002 |
| 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