Designing Sustainable Prosperity “DSP”: A collaborative effort to build resilience in mining producing regions
Bibliographic record
Abstract
Mines are frequently located in remote areas with little conventional employment and few opportunities for the local population. The development and operation of the mines results in several years of intense activity followed by a near-complete reduction in employment and opportunities after the mines are closed. Designing Sustainable Prosperity is a method for rectifying this situation by designing for long-term economic activity in areas that host mines. This process involves the participation of local and national governments, local community, mining companies, investors, academics, and those with sector expertise. The mines will be the catalyst for regional sustainable development. If successful, long-term economic and environmental prosperity should result for the areas affected by mining and could promote the regions as centers of excellence for a particular industry. This paper describes how the concept works using the copper producing region of Peru and Chile as an example. Designing sustainable prosperity starts by looking at regions based on the natural resources and skills available, the infrastructure, and possible energy sources. Integrated natural resource models and innovative market studies, followed by education and skills requirements, are then established to determine the potential for the region and what needs to be done to realize the possibilities.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".