The global mining industry: corporate profile, complexity, and change
Bibliographic record
Abstract
Abstract The continuation and increasing importance of mining is inevitable as society embraces both the transition to a low-carbon economy and application of circular economy concepts. However, across many parts of society, there is an ongoing sense that those who are carrying many of the costs and risks related to mining particularly over the long term (often host communities and countries) are not seeing a level of benefit that seems fair. In contrast, there is frustration within the industry that mining is not being given due credit for the importance of its role in contemporary society by those who would criticize industry practices. Over the past several decades, dozens of initiatives aimed at strengthening mining’s social and environmental performance have been mounted from both within and outside the industry. These generally depend on a “leadership-trickle-down” change model. While progress has been achieved, the society-industry trust deficit continues. The global mining community comprises a corporate core and a complex range of other surrounding interests. We suggest that some key questions regarding the nature of this community and its appetite and capacity for change have not been explored thus impeding the effectiveness of change management. We offer (1) an estimate of the number of companies that lie at the core of the global mining community: some 25,000 operating in about 140 countries (using data from the mid-2010s); (2) a profile of these companies as an initial step towards understanding the “culture” of the global mining community; and (3) a listing of additional complexities and observations important to bringing global-wide improvement to mining’s social and environmental performance. We argue that building on work to date, a fresh approach is required. We are calling for a dialog to reflect on the ideas presented here, refine them as appropriate, and develop the needed strategies and action plans. Such a process must build from a comprehensive understanding of the global mining community and its culture. It must be collaborative in nature and involve not only the range of mining companies but also with surrounding interests and governments. If this is not done, the change that is needed to align actions of all mining actors with social values will not occur and the trust deficit will remain.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".