Tackling injustices of occupational lung disease acquired in South African mines: recent developments and ongoing challenges
Bibliographic record
Abstract
BACKGROUND: South Africa's mineral resources have produced, and continue to produce, enormous economic wealth; yet decades of colonialism, apartheid, capital flight, and challenges in the neoliberal post-apartheid era have resulted in high rates of occupational lung disease and low rates of compensation for ex-miners and their families. Given growing advocacy and activism of current and former mine workers, initiatives were launched by the South African government in 2012 to begin to address the legacy of injustice. This study aimed to assess developments over the last 5 years in providing compensation, quantify shortfalls and explore underlying challenges. METHODS: Using the database with compensable disease claims from over 200,000 miners, the medical assessment database of 400,000 health records and the employment database with 1.6 million miners, we calculated rates of claims, unpaid claims and shortfall in claim filing for each of the southern African countries with at least 25,000 miners who worked in South African mines, by disease type and gender. We also conducted interviews in Johannesburg, Eastern Cape, Lesotho and a local service unit near a mine site, supplemented by document review and auto-reflection, adopting the lens of a critical rights-based approach. RESULTS: By the end of 2017, 111,166 miners had received compensation (of which 55,864 were for permanent lung impairment, and another 52,473 for tuberculosis), however 107,714 compensable claims remained unpaid. Many (28.4%) compensable claims are from Mozambique, Lesotho, Swaziland, Botswana and elsewhere in southern Africa, a large proportion of which have been longstanding. A myriad of diverse systemic barriers persist, especially for workers and their families outside South Africa. Calculating predicted burden of occupational lung disease compared to compensable claims paid suggests a major shortfall in filing claims in addition to the large burden of still unpaid claims. CONCLUSION: Despite progress made, our analysis reveals ongoing complex barriers and illustrates that the considerable underfunding of the systems required for sustained prevention and social protection (including compensation) needs urgent attention. With class action suits in the process of settlement, the globalized mining sector is now beginning to be held accountable. A critical rights-based approach underlines the importance of ongoing concerted action by all.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".