THE ROLE OF FORECASTING STUDIES IN THE UN SUSTAINABLE DEVELOPMENT GOALS AGENDA
Bibliographic record
Abstract
The article is devoted to the role of forecasting studies in the planning, monitoring and achievement of the United Nations Sustainable Development Goals (SDGs) and specific targets, put together in the UN General Assembly resolution “Transforming our world: the 2030 Agenda for Sustainable Development”. Forecasting and exploring potential development scenarios have been used as a basis for the SDGs development; as an instrument for strategic planning and providing recommendations to national governments; and also as a communication tool, aimed at attracting public attention to potential risks and mobilizing resources and funding. The author also focuses on the enhancement of the system of indicators used for monitoring progress in reaching SDGs and the influence that the Millennium Development Goals (MDGs) and later SDSs are having on the development of tools and methodology for monitoring and forecasting. Violent conflicts remain one of the most critical obstacles in moving towards SDGs, not only causing human deaths and suffering, but also ruining health and education facilities, economies and infrastructure and reversing development trends. Conflicts also are difficult to forecast, though different initiatives aimed and predicting, and ultimately preventing violent conflicts are being developed. Because of violence, data for monitoring and forecasting becomes either difficult to access or not available at all. This creates a risk of overlooking the needs of people affected by conflict due to the lack of reliable data. The article also examines an initiative aimed at forecasting long-term effects of violent conflict on development. There are different dimensions in terms of forecasting initiatives connected to the SDGs. The goals and respective targets themselves are based on the analysis of current trends and identifying possible scenarios of development by 2030. Both optimistic and pessimistic forecasts are used for communication with general public and national governments. Data from different countries can be used for identifying good practices and projecting similar trends on other regions. At the same time, the scale of activities connected to SDGs allows to enhance and streamline collection of data globally, thus providing a basis for future forecasting efforts. About the author: Elena M. Kharitonova, Cand. Sci. (Polit.), Senior Researcher, Sector of International Organizations and Global Political Governance, Department of International Political Problems.
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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 | 0.062 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".