Financial technocrats as competitive regime creators: The founding and design of the Network for Greening the Financial System
Bibliographic record
Abstract
Abstract Why did a group of eight central bankers and financial supervisors from across the globe create a Network for Greening the Financial System in 2017? Why did they design this network as they did? The founders were an uncommon coalition, led by French financial authorities working closely with their Dutch, British, and Chinese counterparts, and backed by others from Germany, Mexico, Singapore, and Sweden. They were engaged in an unusual act of competitive regime creation at a moment when the United States was abandoning global leadership vis‐a‐vis climate change. This purpose led them to create a financial transgovernmental network that was distinctive in its roles, membership, and governance. Addressing these questions not only improves understanding of a body that has emerged as a leading voice in global discussions about the environmental aspects of financial policy. It also contributes to wider scholarship on financial transgovernmental networks and the greening of central banks.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.028 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".