Bibliographic record
Abstract
Abstract On 11 September 2012, over 250 workers of Ali Enterprises, which produced jeans for the German discount retailer KiK, perished in a fire in their Karachi factory. Was this an accident or an arson attack? Straight away, the tragedy gave rise to contradictory interpretations. While some blamed the exploitative logics of fast fashion, others suspected foul play by the political parties preying on the city and its business class.Taking as a starting point the controversy caused by this disaster, Gunpoint Capitalism plunges us into the murky waters of globalization. Exploring the back alleys of Pakistan’s industrial capital city, it shows how the manufacturing economy makes order out of disorder, and profit out of conflict–to the detriment of workers. In Karachi, as elsewhere, petty criminals and ex-servicemen prove to be formidable enforcers of economic order. A comparison with Europe, the United States and Latin America confirms the central place of such henchmen in the dynamics of capitalism. These shock troops of anti-unionism are now participating in the dismantling of the social state.This probing, sometimes shocking, book sheds new light on the power structures, organized violence and daily labor struggles underpinning the production of our consumer goods.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".