<b>Amazon’s distribution space: constructing a ‘labour fix’ through digital Taylorism and corporate Keynesianism</b>
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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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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.005 |
| 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