Bibliographic record
Abstract
As the global economic recession deepens, the structure of financial institutions and the legal principles that they apply are of primary concern to investors.One aspect of the legal debate has focused on whether financial market regulation should be based on principles or rules.Generally, principles-based regulation refers to a broad set of standards that gesture in the direction of certain desired outcomes.These standards may be accompanied by guidelines about how to achieve the outcomes.By contrast, rules-based regulation is, as the name implies, based on a set of detailed rules that govern firms' behavior.Such rules enable firms to "tick-the-box" to guarantee compliance with law.Another possibility-institution-based financial regulation-has recently been proposed by John Walsh as an alternative to rules and principles. 1 This approach appears to have two parts.First, the approach refers to offices that firms are legally mandated to establish.For example, the Securities and Exchange Commission (SEC) and the Financial Industry Regulatory Authority (FINRA) require firms to establish certain offices and structures (the "institutions" to which Walsh refers) such as the Chief Compliance Officer, compliance policies and procedures, and annual selfassessments.Second, these firms will by necessity have firm-specific modus operandi or ways of functioning.The institutional approach provides them with flexibility in terms of how the required structures evolve and operate within the organization.
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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".