Writing Back Against Amazon’s Empire: Science Fiction, Corporate Storytelling, and the Dignity of the Workers’ Word
Bibliographic record
Abstract
Since its founding in 1994 as an online bookstore, Amazon has “revolutionised” not only the market for literature but also expanded aggressively and transformatively in sectors including consumer retail, film and television, groceries, logistics, robotics, surveillance, AI, and web services. This growth and expansion is grounded in the firm’s internal and outward-facing rhetoric about its leading contribution to a brighter future, a narrative deeply inspired by the genre of science or speculative fiction (SF). But Amazon’s utopian vision is largely experienced as a dystopia by most of its rank-and-file workers, who labour under exploitative conditions of surveillance, robotization, and relentless managerial control. Hence our team inaugurated the Worker as Futurist project to support rank-and-file Amazon workers to read/watch SF stories to collectively understand their employer and its world, and also to write short, SF stories about “the world after Amazon.” In this preliminary report on the project, we explain the inspirations for the project and reflect on some of what we have learned from the participants, as well as some implications for the futures of platform workers generally.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.025 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".