Bibliographic record
Abstract
How should we think about regulating our dynamically changing financial system? Existing regulatory approaches have two temporal flaws. The obvious flaw, driven by politics and human nature (and addressed in other writings), is that financial regulation is overly reactive to past crises. This article addresses a less obvious but arguably more fundamental flaw: that financial regulation is normally tethered to the financial architecture, including the distinctive design and structure of financial firms and markets, in place when the regulation is promulgated. In order to effectively address future crises, this article argues, financial regulation must transcend that time-bound architecture. This could be done by regulating the underlying economic functions of the financial system—the provision, allocation, and deployment of financial capital—as well as the financial system’s capacity to serve as a network within which those functions can be conducted. The article analyzes how to design and implement such a “functional” approach to financial regulation. Although this approach is primarily normative, it provides regulatory ordering principles that should have practical utility—not only as a set of standards to inform actual regulatory design but also as a counterweight to the prevailing view that macroprudential regulation of systemic risk can be adequately served by an ad hoc assortment of regulatory “tools.”
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 | 0.018 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".