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Record W7062220226

Statutory civil liabilities of corporate gatekeepers for defective prospectuses in Australia, the United States, the United Kingdom and Canada: a comparison

2014· article· en· W7062220226 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2014
Typearticle
Languageen
FieldEngineering
TopicThermal Analysis in Power Transmission
Canadian institutionsnot available
Fundersnot available
KeywordsProspectusIssuerInvestment bankingBroker-dealerCommissionSecurities Exchange Act of 1934DisappointmentAuditPlaintiffStatutory law
DOInot available

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.380
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.053
GPT teacher head0.249
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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