Bibliographic record
Abstract
This article presents an analysis of the G20 which, while recognising the innovative capacity of this leaders’ forum, also addresses one of its major sources of contestation, the lack of equitable regional balance. Whereas the EU has an overrepresentation within the G20 membership, other regional constituencies including the Caribbean, the Nordics, Southeast Asia and Africa have been underrepresented or excluded completely. This has consequences in terms of heightening the legitimacy gap already in place due to the summit’s image as a self-selected concert of big powers. Yet the excluded regions have advocated inclusion, not rejection, of the G20. The main focus of the article is therefore on the practical means by which forms of inclusion should and can be enhanced. To its credit, South Korea as host of the November 2010 G20 has moved to address some of the problems associated with these representational imbalances. But the search for inclusion needs to be stepped up to enhance the G20’s position as a model of legitimate global governance. Moreover, many of the mechanisms to promote inclusion are feasible, taking advantage of the flexibility in the design of the G20 project as it has evolved over the past two years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.027 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".