The Group of 20 transnational policy community: governance networks, policy analysis and think tanks
Bibliographic record
Abstract
The G20 is an evolving international institution. Aided by both advances in information technology and support from home governments, a number of knowledge actors and networks seek to influence global economic governance with policy analysis and advice. This article assesses the international G20 think tank network called Think20 and the policy advocacy of private research institutes (such the Lowy Institute in Australia and the Centre for International Governance and Innovation in Canada) which are in the orbit of the G20 policy community. Think20 assists the global economic governance processes of the G20 by developing ‘coordinative discourses’ for policy development and implementation. Points for practitioners Ideas matter but ideas that imply major policy reform and innovation need to be made to matter if they are to direct government action. Networks provide one mechanism to broadcast and disseminate ‘communicative discourses’ to many different publics – local as well as global – and the infrastructure to crystalize and amplify ‘coordinative discourses’ underpinning the policy blueprints and reform proposals of governments that must motivate their own bureaucracies as well as collaborate with foreign government. This article discusses some of the network tools and practices for consensus building in ‘transnational policy communities’ that cultivate global policy coordination.
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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 | 0.029 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.005 |
| 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".