Review of: Jason Resnikoff (2021) Labor's End: How the Promise of Automation Degraded Work
Bibliographic record
Abstract
A century after Arthur Pound (1922) published The Iron Man in History, considered the first study of automation, Jason Resnikoff has written a comprehensive history of the same topic and American labour.He argues that although automation promised more leisure, a term he finds problematic because it insinuates that work is the default setting of mankind, it actually degrades work.The book is a history of the automation discourse just as much as of automation itself.The author traces a paradox: though automation was supposed to make for a better society, it actually led to the speeding up and increased exploitation of labour.Many automation analysts were starryeyed, believing in a utopian world of less work for the same amount of pay.Some believed it would lead to idleness and a lack of purpose, as machines would fill all the roles humans heretofore had.Freedom was thus incompatible with work.But automation could create "a race of natural slaves constitutionally incapable of rebellion" (p.143).Humanity would be turned into "cheerful robots", as C. Wright Mills described them (p.160).Resnikoff's book is much-needed, as modern discussion on automation in the labour movement is scant at best.Historians have largely ignored it.Meant for specialists, this book will serve as a reference for all future historians looking to study automation.Resnikoff disagrees with the "technological determinist milieu" put forward by earlier commentators (p. 1).He argues, "The current use of the idea of 'automation' allows critics to sidestep the question of how power should be distributed at the workplace today and to speak instead of the possible effects of one type of mechanism on another" (p.192).For Resnikoff, globalisation and overproduction, not automation, were the main sources of job loss: "When at the turn of the twenty-first century, managers shipped factories to the right to work South, to Mexico, and to China, they left in pursuit not of 'automation' but cheap human labor" (p. 6).Labor's End is well-sourced, relying heavily on the Walter P.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".