From Silos to Synergies: G20 Governance of the SDGs, Climate Change & Digitalization
Bibliographic record
Abstract
How well and why have Group of 20 (G20) summits advanced Agenda 2030’s sustainable development goals (SDGs) in a synergistic way, with climate change and digitization at the core? An answer to this urgent, indeed existential, question comes from a systematic analysis of G20 summit governance of the SDGs, climate change and digitization to assess the ambition and appropriateness of advances within each pillar and the synergistic links among them. This analysis examines G20 governance of the SDGs, sustainable development, climate change and digitization across the major dimensions of performance and evaluates how performance has changed and become synergistic with the advent of the SDGs in 2015 and the shock of the COVID-19 crisis in 2020. The latter has shown the need to prevent global ecological crises and spurred the digitization of the economy, society and health. Yet, G20 summit governance has largely remained in separate silos, doing little to use the digital revolution to address climate change or reach the SDGs. This highlights the need for G20 leaders to forge links at their future summits by mainstreaming the SDGs and mobilizing the digital revolution and climate action for future health and well-being.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".