Algorithmic Management and Workers’ Responses: A Systematic Review and Thematic Analysis
Bibliographic record
Abstract
With the emergence of digital labour platforms (DLPs), we have witnessed a series of studies about DLPs, platform workers and algorithmic management (AM). In the current review, we summarize and synthesize the impact of each function of AM on workers, as well as workers’ responses to it. We find that AM in effect limits workers’ scheduling autonomy, work method autonomy, criteria autonomy, task choice autonomy, and locational autonomy. In addition, we also find some other impacts of AM, such as work precarity, unfairness and stress. We also notice the differences between web-based and location-based platforms, especially regarding workers’ autonomy. Regarding their responses, we discuss their compliance, unpaid labour, resistance, algoactivism, and sensemaking behaviours. We conclude that platform workers are virtual employees of virtual factories managed by invisible managers. Last, we suggest several study questions that appeal to a brighter future of platform work and workers and study questions that help scholars better understand platform workers’ behaviours.
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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 | 0.026 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.025 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".