Challenges at the Intersection of Mineral Resource Sector, Circular Economy, and Economic Development
Bibliographic record
Abstract
This chapter explores the intricate interplay between sustainability challenges in the mineral resource sector, the evolving concept of the circular economy, and the implications of population growth. It delves into the environmental, social, and economic dimensions, aiming to provide insights into the complexities and potential solutions at the nexus of these critical global issues. The extraction and processing of minerals are fundamental to modern technology and infrastructure. To mitigate the environmental impact of increased consumption, it is crucial to adopt circular design principles and promote the use of durable, repairable, and recyclable products. Sustainability in the mining sector requires a comprehensive approach that considers social, economic, and environmental factors. Collaboration among governments, industry stakeholders, and communities is vital to addressing present and future challenges. The mineral resource sector can support global development while safeguarding the planet by integrating sustainable practices, embracing technology, and prioritising ethics. We have interpreted the problem from an engineering perspective, devising a blueprint for an (un)sustainable machine model that explains the relationship between the demand, consumption, and recycling of minerals and Earth’s planetary boundaries. The (un)sustainable machine model hides a significant truth. While there is no doubt that human activities impact Earth’s planetary boundaries, it is difficult to see how any of these impacts could be genuinely benign.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".