Transitioning to Strong Partnerships for the Sustainable Development Goals
Bibliographic record
Abstract
Transitioning to Strong Partnerships for the Sustainable Development Goals is an edited volume dedicated to current developments regarding SDG 17 “Partnerships for the Goals”. This goal contains preconditions and systemic issues that will facilitate the success of the SDGs in general. Thus, the volume covers conditions, structures, and means to strengthen the SDGs from both the theoretical and practical perspective. Transitioning to Strong Partnerships for the Sustainable Development Goals has three main focal points: Theoretical approaches to sustainable partnerships, including public–private partnerships. Different structural aspects for sustainable partnerships, including financial deals with philanthropic initiatives and new financing models as well as new technologies to meet the logistical challenges of development aid. Systemic issues, especially institutional coherence, multi-stakeholder approaches, and challenges of statistics for development. The invited academic and practical contributions cover a variety of different fields of research and expertise. Transitioning to Strong Partnerships for the Sustainable Development Goals is part of MDPI's new Open Access book series Transitioning to Sustainability. With this series, MDPI pursues environmentally and socially relevant research that contributes to efforts toward a sustainable world. Transitioning to Sustainability aims to add to the conversation about regional and global sustainable development according to the 17 SDGs. The book series is intended to reach beyond disciplinary, even academic boundaries.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.061 | 0.023 |
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".