New Governance in the Teeth of Human Frailty: Lessons from Financial Regulation
Bibliographic record
Abstract
New Governance scholarship has made important theoretical and practical contributions to a broad range of regulatory arenas, including securities and financial markets regulation. In the wake of the global financial crisis, question about the scope of possibilities for this scholarship are more pressing than ever. Is new governance a full-blown alternative to existing legal structures, or is it a useful complement? Are there essential preconditions to making it work, or can a new governance strategy improve any decision making structure? If there are essential preconditions, what are they? Is new governance “modular” – that is, does it still confer benefits when applied partially or imperfectly, or does it fail to achieve good regulatory results unless all the elements are in place? This article starts from the conviction that new governance is a promising response to the fluidity and complexity of contemporary regulatory environments. It then draws on three essentially unhappy narratives from recent financial markets regulation (around securities law enforcement, capital adequacy, and the impact of securitization), in an attempt to identify lessons for new governance scholarship at the level of practical implementation. These are not narratives about the failure of new governance structures. However, central to each narrative are components, or incomplete versions of components, that are also central to new governance structures. The paper considers the significance of incrementalism, regulatory capacity, and destabilization and complexity for regulatory design. It closes with some preliminary recommendations for making new governance structures effective, even as implemented by flawed human actors.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".