Designing Sustainable Prosperity “DSP”: A collaborative effort to build resilience in mining producing regions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it