Bibliographic record
Abstract
Abstract Corporate rules are often analysed without attending to the strengths and limitations of the body making, monitoring or implementing those rules. However, corporate rule-making and implementation bodies (RMIBs) over which policymakers have the most influence—legislatures, public regulatory agencies, stock exchanges, and private/professional bodies with a degree of self-regulatory autonomy—have an important bearing on the effectiveness of rules. This article advances a framework to understand how RMIBs influence the effectiveness of corporate rules by critically examining five core features of RMIBs: (a) their incentives for making and implementing the rules; (b) the nature and extent of regulatory competition; (c) available and relative resources; (d) rule-making speed and the certainty of their decisions; and (e) their legitimacy in the eyes of the regulated parties and relevant stakeholders. To illustrate the framework concretely, this article conducts case studies exploring how it matters who makes the rules on climate-related risks disclosure and in the UK’s recently enacted Financial Services and Markets Act 2023.
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 | 0.051 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.027 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".