Bibliographic record
Abstract
Tried-and-tested approach for transforming economies that are based on natural resource extraction to create long-term prosperity Designing Sustainable Prosperity (DSP) is a step by step blueprint for transforming economies from being dependent on short term natural resource extraction into long-term sustainable prosperity. The outcome is the creation of sustainable, circular economies that prioritise waste reduction and use recycling and renewable resources to actively implement climate change solutions. A key feature of the process is collaboration between local people, investors, appropriate experts, government and academics. The DSP method outlines seven steps in creating a plan for long-term sustainable regional development, illustrated by several case studies from North and South America which identified potential economic transformations. Designing Sustainable Prosperity explores topics such as: How to determine if and when a region is ready for DSP by analyzing factors such as climate, geology, natural resources and human potentialCase studies highlighting different aspects of the DSP approach, and how to achieve true prosperity which is beyond short-term financial performance “Hard” resources and industries that can fuel a circular economy, such as metals/mining, water/ energy, value added food products and other innovative enterprises “Soft” enabling factors such as workforce availability, educational systems, and socio-economic conditions and how to develop these factors in line with the United Nations Sustainable Development Goals (UNSDGs) DSP shows how to align economic goals with all the UNSDGs. Designing Sustainable Prosperity is an essential and timely resource for professionals and organizations aiming to develop regions sustainably. “Not only a great collection of ideas and references but also a great story in terms of how it brings the pieces together and guides how we can each make a difference.” —Mark Cutifani, Chairman Vale Base Metals, Former CEO at Anglo American plc “Recommended for corporations, politicians and regulators to understand the sequencing necessary to access the energy transition metals and realize 2050 aspirations in a sustainable manner.” —Robert Quartermain, DSc, Canadian Mining Hall of Fame Inductee, Co-Chair Dakota Gold Corp, Former Executive Chairman Pretium Resources Inc “Presents an optimistic, “bottom up” collaboration recipe that leavens outside expertise with community-based history, capabilities, and ambition to move in new directions.” —David J. Hayes, Professor at Stanford University, former senior White House climate advisor for President Biden and the Deputy Secretary and Chief Operating Officer of the U.S. Department of the Interior for Presidents Obama and Clinton
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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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".