Mining Migrant Worker Recruitment Policy and the Production of a Silicosis Epidemic in Late 20th-Century Southern Africa
Bibliographic record
Abstract
Objectives: Between the 1980s and 2000s, an epidemic of silicosis was identified in migrant black gold miners, many from neighbouring countries, who had worked in the South African gold mines. This study uses the newly available employment database of a large gold mining company to demonstrate how a sustained rise in employment duration in a new cohort of black migrant workers resulted from changes in recruitment policy, and it examines the implications for current surveillance and redress. Methods: Contract data of 300,774 workers from the employment database of a multi-mine gold mining company were analysed for 1973-2018. Piecewise linear regression was applied to determine trends in cumulative employment, including South African versus cross-border miners. The proportions with cumulative employment of at least 10, 15, or 20 years, typical thresholds for chronic silicosis, were also calculated. Results: Five calendar phases were identified between 1973 and 2018. During the second phase, 1985-2013, mean cumulative duration of employment rose fivefold, from 4 to 20 years. Cumulative employment continued to rise, although more slowly, before peaking in 2014 at 23.5 years and falling thereafter to 20.1 years in 2018. Over most of the 1973-2018 period, miners from neighbouring countries had greater cumulative employment than South African miners. Overall, the proportion of miners exiting with at least 15 years of cumulative employment rose from 5% in 1988 to 75% in 2018. This report identifies a number of fundamental changes in labour recruitment policy in the gold mining industry in the 1970s which provide an explanation for the subsequent rise in cumulative exposure and associated silicosis risk. Conclusions: These new data support the hypothesis of a silicosis epidemic driven by increasing cumulative silica dust exposure in a new cohort of circular migrant workers from the 1970s. They inform current programmes to improve surveillance of this neglected population for silicosis and related disease and to provide medical examinations and compensation to a large number of former gold mines. The analysis highlights the lack of information on cumulative employment and silicosis risk among migrant miners in previous decades. The findings have global relevance to the plight of such migrant workers in hazardous occupations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".