<b>Amazon’s distribution space: constructing a ‘labour fix’ through digital Taylorism and corporate Keynesianism</b>
Bibliographic record
Abstract
Abstract Amazon is one of the largest e-commerce corporations in the world and has built a reputation for fast, low-cost service. To rapidly and efficiently move goods from production to consumption, however, Amazon relies on a logistics network that entails significant investments in infrastructure (physical and human) and these investments present a challenge for capital accumulation. In this paper, I examine the labour practices that Amazon employs within its distribution work spaces to address this challenge. The analysis is based on a case study of Amazon’s distribution facilities (fulfilment centres and delivery stations) in Montreal, Quebec. It draws on ethnographic research as a community organizer and semi-structured interviews with workers (present and former), trade union representatives and public policy experts to identify Amazon’s key strategies. Building on past studies on the platform economy, I illustrate how Amazon relies on ‘digital Taylorism’ (Staab & Nachtwey, 2016), involving the use of digital technologies to structure and control the labour process and surveil workers, as a key strategy. However, I further illustrate how Amazon seeks to balance the harmful effects of digital Taylorism with what I term ‘corporate keynesianism’ (i.e., social welfare benefits) to attain a ‘labour fix’, i.e., the steady supply of precarious, compliant labour needed to sustain the logistics machine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".