“Owned them like a Father": Labor Contractors, Port Workers, and the Makings of Ethnicity in Singapore
Bibliographic record
Abstract
Despite the importance of ports to the Indian Ocean world, labor contracting systems in ports remain understudied. By focusing on ethnic divisions of labor among port workers in colonial Singapore from the 1930s to the 1950s, this article shows how labor contractors constructed these divisions and how social organization in modern Singapore is rooted in labor contracting at the port. Past scholarship has explained merchants’ propensity to form partnerships within the same kin or ethnic circles with the notion of trust: that those of the same kin or ethnicity could be trusted more easily. However, this article argues that labor contractors often recruited migrant workers from the contractors’ home villages and regions because shared kinship and ethnicity allowed contractors to better control workers’ laboring, social, and cultural life. Performances of shared kinship and ethnicity gave contractors power as both employers and community leaders. After World War II, port workers also solidified ethnic divisions by organizing into unions along the lines of ethnicity, and they secured benefits as ethnic blocs, rather than for all port workers. This post-war moment of organizing labor by ethnicity has shaped labor activism in Singapore today as migrant workers continue to strike in ethnic blocs to protest disparities in working conditions between workers of different ethnicities.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".