Review of: Jenny Chan, Mark Selden and Pun Ngai (2020) Dying for an iPhone: Apple, Foxconn, and the Lives of China’s Workers
Bibliographic record
Abstract
In Dying for an iPhone, the authors show that the lives of Foxconn workers are constrained by managerial policies, which persistently emphasise fast production and high product quality.Because it is very difficult to meet these two objectives at the same time, workers at Foxconn often had to work long hours under high labour intensity.This caused heavy physical and psychological stress among the workers, which resulted in worker suicides.To find more fundamental causes of the workers' toilsome life, the authors go beyond the wall of Foxconn factories.They extend their scope of research from shop-floor politics to the national and global connections among the Chinese state, multination corporations and customers outside China.In fact, the ultimate purpose of this book is to inform people of labour issues hidden behind the transnational chains that combine production with consumption.By dissecting the buyer-driven business model, this book attempts to inspire transnational activism to oppose labour appropriation wherever it is found.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.037 | 0.022 |
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".