The G20 and Global Economic Governance: Lessons from Multi-Level European Governance?
Bibliographic record
Abstract
International cooperation is, in the long run, a necessary ingredient in the search for national prosperity. This should lead every country to look with a renewed sense of responsibility and discipline to the system as a whole. The G20 […] would be in a powerful position to promote the global common good, and to make it prevail, including, at times, against a narrow, short-term interpretation of national interests. 1 Just as the Eurozone is a microcosm of the global economy, the dysfunctional G20 is a macrocosm of the European Council. Its members, European Union leaders, also gather for high-profile summits. Each time they promise comprehensive solutions and fail to deliver. The parallels are remarkable. 2 It has been rightly observed that ‘[g]lobalization … contribut[es] to the transformation of ever more local and national goods into international public goods requiring new forms of governance, regulation and justification.’ 3 The present article aims to enrich the debate on multi-level governance of interdependent public goods with lessons drawn from the most advanced regional integration project, the EU. We discuss the insights gained from ‘multilevel European governance’ for global economic governance and, more specifically, for the collective action problems involved in procuring international public goods. Furthermore, we specifically reflect on the question whether, drawing on the European experience, it is expedient for the G20 to be institutionalized as a ‘global economic governance executive’.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".