A Working Paper for the G-20 Development Working Group, Pillar Nine
Bibliographic record
Abstract
The objective of this paper is to recommend, at the request of the Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, South Korea, Mexico, Russia, Saudi Arabia, South Africa, Turkey, the United Kingdom, and the United States - along with the European Union (EU) (G-20), how knowledge sharing (KS), through North-South, South-South, and triangular cooperation, can be scaled up in support of growth and development processes. The working group collaborates closely with the steering committee for pillar nine, which, besides the working group members, also includes Korea and Mexico as co-facilitators for pillar nine, France as this year’s G-20 Chair, and Colombia and Indonesia as co-chairs of the task team on South-South cooperation. The paper is organized into four sections: section one presents a description of how KS is increasingly viewed as a complementary third leg to financial and technical cooperation in the changing global development landscape; section two presents a set of emerging, evidence-based lessons for KS as a development tool; section three recommendations from the G-20 to scale up KS; and section four presents next steps on the short and medium term.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.053 | 0.039 |
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".