Cleaning the wharves: Pilferage, bribery, and social connections on the Durban docks in the 1950s
Bibliographic record
Abstract
This article looks at practices of pilferage and bribery among African migrant dock workers in Durban in the 1950s. Many of Durban's dockers regularly engaged in small-scale theft, usually food for personal consumption, but sometimes they also got their hands on bigger and more expensive items or sold the pilfered goods. Many also relied on their social networks to find jobs and did not shy away from bribing izinduna to make sure that they were hired on ships that contained the right goods. Such crimes, which were often not recognised as such by the workers, have often been seen as forms of primitive and individual resistance to proletarianisation. This article, however, argues that these were not just reactive and opportunistic acts, but part of a conscious strategy to combine dock labour with a small business, which allowed several workers to withdraw from the wage labour market altogether.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| 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".