Bibliographic record
Abstract
<JATS1:p>A positive agenda for achieving the Sustainable Development Goals by 2030All 193 member nations of the United Nations agreed in September 2015 to adopt a set of seventeen "Sustainable Development Goals," to be achieved by 2030. Each of the goals—in such areas as education and health care —is laudable in and of itself, and governments and organizations are working hard on them. But so far there is no overall, positive agenda of what new things need to be done to ensure the goals are achieved across all nations. In a search of fresh approaches to the longstanding problems targeted by the Sustainable Development Goals, the Japan International Cooperation Agency and the Global Economy and Development program at Brookings mounted a collaborative research effort to advance implementation of Agenda 2030. This edited volume is the product of that effort. The book approaches the UN's goals through three broad lenses. The first considers new approaches to capturing value. Examples include Nigeria's first green bonds, practical methods to expand women's economic opportunities, benchmarking to reflect business contributions to achieving the goals, new incentives for investment in infrastructure, and educational systems that promote cross-sector problem solving. The second lens entails new approaches to targeting places, including oceans, rural areas, fast-growing developing cities, and the interlocking challenge of data systems, including geospatial information generated by satellites. The third lens focuses on updating governance, broadly defined. Issues include how civil society can align with the SDG challenge; how an advanced economy like Canada can approach the goals at home and abroad; what needs to be done to foster new approaches for managing the global commons; and how can multilateral institutions for health and development finance evolve.</JATS1:p>
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".