Access of migrant gold miners to compensation for occupational lung disease: Quantifying a legacy of injustice
Bibliographic record
Abstract
BACKGROUND: A legacy of the South African gold mining industry, now in decline, is a large burden of silicosis and tuberculosis among former migrant miners from rural South Africa and surrounding countries, particularly Lesotho and Mozambique. This neglected population faces significant barriers in filing claims for compensation for occupational lung disease. The objective of the study was to gain insight into the extent of such barriers, particularly for former miners and cross-border migrants. METHODS: The database of a large gold mining company and the statutory compensation authority were analyzed for the period 1973-2018 by country of origin, age, and employment status at the time of claim filing. Proportions and odds ratios (ORs) for each of the compensable diseases were calculated by the above variables. Processing delays of claims were also calculated. RESULTS: Annual company employment declined from 240,718 in 1989 to 43,024 in 2018 and the proportion of cross-border migrants within the workforce from 51.0 to 28.1%. The compensation database contained 68,612 claims. The majority of compensable claims in all diagnostic categories were from active miners. The odds of cross-border miners relative to South African miners filing a claim depended on employment status. For example, the OR for Lesotho miners filing while in active employment was 1.86 (95% CI 1.81, 1.91), falling to 0.94 (95% CI 0.91, 0.98) among former miners. The equivalent findings for Mozambiquan miners were 0.95 (95% CI 0.91, 1.00), falling to 0.44 (95% CI 0.41, 0.47). Median processing delays over the whole period were from 1.1 years from filing to adjudication, and 3.8 years from filing to payment. CONCLUSIONS: The findings provide a quantitative view of differential access to occupational lung disease compensation, including long processing delays, among groups of migrant miners from the South African gold mines. There is a deficit of compensable claims for silicosis and silico-tuberculosis among former miners irrespective of country of origin. While cross-border miner groups appear to file more claims while active, this is reversed once they leave employment. Current large-scale efforts to provide medical examinations and compensation justice to this migrant miner population need political and public support and scrutiny of progress.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".