Statutory civil liabilities of corporate gatekeepers for defective prospectuses in Australia, the United States, the United Kingdom and Canada: a comparison
Bibliographic record
Abstract
Securities regulation is largely the regulation of information asymmetry in relation to the selling of financial assets described as securities. This selling requires information concerning issuers and their securities to be disclosed to the investing public. Securities regulation seeks to regulate this disclosure in order to ensure a level playing field between issuers and their potential investors. The House of Lords in Peek v Gurney held in 1873 that the objective of a prospectus was to enable investors to make an informed investment decision.' Most of the recent corporate failures in the United States between 2001 and 2002 such as Enron, WorldCom, Tyco, HealthSouth and Adelphia resulted from financial scandals in which issuers attempted to maximise the price of their securities by creating misimpressions about their financial health. Very recently, the Australian Securities and Investment Commission (ASIC) has expressed disappointment at "the significantly worse performance of auditors" found in an 18-month audit of auditors conducted by the regulator.' This malpractice is sometimes a deliberate fabrication of material facts, while other times it is a product of recklessness or negligence on the part of persons involved in the preparation and issuance of disclosure documents. Such a practice has a perilous impact upon the integrity of, and investor confidence in, the market.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".