Reforming Financial bench marks: an i nternational Perspective
Bibliographic record
Abstract
Robust benchmarks are of fundamental importance to financial markets, providing objective measures of prevailing market prices on which standardized contracts can be based. They are especially important to derivatives markets, since derivatives are an essential hedging tool for financial institutions and other market participants; the notional value of these instruments amounts to hundreds of trillions of dollars worldwide, including over $10 trillion in Canada. Allegations of the manipulation of some global finan-cial benchmarks and, in some cases, admissions of wrongdoing have captured the attention of the world’s financial press, clearly highlighting the need to address the incentive problems and weak governance affecting some benchmarks. Central banks and other public authorities around the world, including those in Canada, are working toge-ther to improve financial benchmarks by ensuring that they meet robust international standards. However, given the central role that these benchmarks play in today’s financial system, any substantive changes to them need to be globally coordinated and their broader financial stability implications carefully considered.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".