Prime Suspect: Mechanisms of Labor Control at Amazon's Warehouses
Bibliographic record
Abstract
What mechanisms has Amazon deployed in its effort to control the labor of its warehouse employees? This question holds both practical and theoretical interest, given Amazon's prominent position in the economy and the wider importance of the logistics sector for consumer capitalism. This paper, part of a broader mixed-methods study of Amazon's workplace regime, uses a small national sample of interviews with Amazon warehouse workers (N = 46) to identify the mechanisms of labor control the company invokes. In keeping with accounts propounded by activists and journalists, we find evidence of highly coercive labor controls, chiefly in the form of what we call techno-economic despotism (which applies algorithmic technology to a precariously employed workforce). Yet many workers also experience forms of labor control that rely not on coercion but on the generation of consent. We identify three such mechanisms of hegemonic labor control - normative, relational, and governmental – that Amazon uses to foster workers’ consent. The efficacy of Amazon's workplace regime stems largely from its ability to deploy a multiplicity of labor controls that resonate with different groups holding distinct positions in the labor process. Given shifts in the social and economic conditions that bear on the company's regime, cracks have begun to appear in Amazon's armor, potentially reducing the traction its labor control mechanisms have gained with segments of its employees.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".