Algorithmic Controls and their Implications for Gig Worker Well-being and Behavior
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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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".