Bibliographic record
Abstract
The mining industry provides valuable mined commodities and financial support for communities worldwide. Mining has become safer for workers. Significant injustices, however, are created by mining companies for workers, local communities and the environment. Mining workers are amongst the world’s most vulnerable because of the dangerous nature of their work, inherent health risks, and problematic, neo-colonial ways in which the industry is governed. Given the scope of these problems, solutions are often challenging, yet this article proposes various responses to global mining inequities. In this article, examples of safe and adequately compensated programs to improve workers’ rights, environmental impacts, and social conditions related to mining will be discussed. Here, solutions to some problems caused by mining are examined, with a focus on workers’ health and human rights through unions and cooperatives, targeted programs for improving mental well-being, the feminization of the mining workforce; the possibility of reducing demand for mining products through reuse and reducing consumption. Ameriolating mining governance is key, through enhanced implementation of human rights, safety, and labor standards, specifically applying the Universal Declaration of Human Rights (UDHR) to mining issues; mining justice organizations play a vital role, particularly in accountability and publicity of mining issues. Canada is spotlighted here as it houses approximately 75% of mining company headquarters, primarily due to favorable tax and investment conditions and the concentration of skilled labor. Greater unionization and cooperativization of mining workers hold great promise for improving health and safety conditions of miners. Feminizing the mining workforce promises to improve both productivity and profits. While mental health is often ignored, Australia’s Mates in Mining program has improved mental well-being and reduced depression and suicide amongst mining workers. Expansion of such programs worldwide would positively impact workers, their families, and improve productivity. Moreover, third-party certification for mining workers’ rights, such as Fairmined, ought to be expanded while universal human rights declarations ought to be upheld. Social justice movements improve worker’s rights, environmental impact and social conditions related to mining. In this article, the importance of improving larger socioeconomic and political conditions in which mining workers operate are also examined, such as reducing demand for mined commodities and recycling more effectively. Improving Indigenous land-based rights is another crucial aspect of creating more just mining practices. Mining offers many workers, particularly those in Canada and other high-income nations, a decent income and benefits. Equivalent wages and benefits ought to be paid to all mining workers worldwide. “It is possible to move from tunnel vision-profit-oriented mining practices which damage workers, communities and environment, to light at the end of the tunnel-- healthy workers, communities and environment”.
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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 | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".