Dock Labour and a Connected History of Workers in Early Twentieth Century Calcutta
Bibliographic record
Abstract
This article argues that class formation and labour radicalism in the industrial cities of colonial India need to be located in connected histories of workers, which go beyond analysis of single industries. It shows that the horizontal mobility of workers in early twentieth-century Calcutta was a result of a pervasiveness of casual work, both among the ‘unskilled’ and the skilled. Skill levels and occupations were crucial in defining the boundaries of not one, as is frequently posited, but several labour pools. It was in this form that the reserve army of labour was ever-present in the city, which gave workers networks beyond one workplace, one neighborhood and frequently, even one industry. The special role of segments of skilled workers has rarely been studied in relation to labour militancy and politics. The article sustains an emphasis on the role of industrial centres, such as the docklands, through which a high degree of interconnectedness across industrial processes in terms of shared occupations and skills across several industries and neighborhoods, can be excavated and mapped onto episodes of labour militancy. The neighbourhood, the trade unions, and nationalist events have all hitherto been studied to understand the shaping of workers’ protest. This article, by contrast, focuses on other crucial elements: the workplace and the industrial processes, which tied workers together in concrete, everyday, and proximate relationships.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.021 | 0.017 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".