Bibliographic record
Abstract
Evaluation is increasingly important for finding sustainable solutions for the people and the planet, based on a systematic analysis of what works, for whom, and under what circumstances, and to contribute to the achievement of the Sustainable Development Goals, as they pertain to the environment. This book explores why the Global Environment Facility (GEF) invests in evaluation for accountability and learning to inform its decision-making on programming priorities, and how this leads to wiser funding decisions and better program performance on the ground. The book is based on real-life experiences of how to make evaluation count for international environmental action. Drawing upon comprehensive evaluations of the GEF, it provides unique insights from authors responsible for designing, implementing, and disseminating the findings of the evaluations. No other multilateral development or environment agency places evaluation fully at the center of their decision-making. The book outlines the trends in the global environment and the changing landscape of international environmental finance. It defines the role of the GEF and explains its institutional framework and the unique partnership that involves donor and recipient countries, multilateral development banks, UN agencies, nongovernmental organizations (NGOs), and national agencies in the developing countries. Further, it provides useful pointers to other organizations wishing to enhance evidence-based decision-making for improving their relevance, performance, and impact. The book will be most suitable for graduate-level, specialized study in a variety of disciplines such as environmental and development economics, political science, international relations, geography, sociology, and social anthropology.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.870 | 0.805 |
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; the direct Gemma label and the distilled Codex classifier 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".