Prospects for scalability: Relationships and uncertainty in responsive regulation
Bibliographic record
Abstract
Abstract Many of the very significant insights in I an A yres and J ohn B raithwaite's 1992 book, Responsive Regulation , have transcended the book's time. At the same time, on the 20th anniversary of its publication, two things about the book are striking. The first is the direct, personal relationship on which the regulatory interaction is premised. The second is the boundedness and manageability of the regulatory project. At least in prudential regulation of global financial institutions in the wake of the recent financial crisis (though surely elsewhere too), neither of these features can be taken for granted. This brief essay seeks to open a preliminary conversation about responsive regulation in terms of its scalability. It considers whether as a practical matter, responsive regulation can be scaled up to more diffuse, multiparty, logistically complex contexts, such as financial regulation. As a matter of representation, it asks whether by projecting the focal object, the responsive relationship, outward, responsive regulation distorts our image of regulation in other contexts (or even in responsive regulation's own home environment). The essay closes by arguing that in order to incorporate responsive regulation's considerable discursive and relational benefits into regulatory environments like global financial regulation, it needs to be buttressed by additional regulatory technologies.
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 | 0.030 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.016 | 0.026 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".