MétaCan
Menu
Back to cohort
Record W1974073694 · doi:10.1350/clwr.34.1.1.60195

Takeover Bid Transactions and Information Asymmetry: Assessment of the Efficiency of the Investment and Securities Act 1999

2005· article· en· W1974073694 on OpenAlexaboutno aff
Ige Omotayo Bolodeoku

Bibliographic record

VenueCommon Law World Review · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Insolvency and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsShareholderLegislatureBusinessDatabase transactionInformation asymmetryLegislationAccountingTender offerInvestment (military)Value (mathematics)Inside informationFinanceLawCorporate governancePolitical science

Abstract

fetched live from OpenAlex

The article examines the value of information to the decisions shareholders are expected to make in takeover bid transactions. In particular, it addresses the facilitative role of the target board in Nigeria which the governing legislation—the Investment and Securities Act (ISA) 1999—expects the board to play through the directors' circular. The article points out that, while it is commendable for the Nigerian legislature to import takeover bid regulations from abroad, the provisions of the ISA dealing with the directors' circular demonstrate a total lack of understanding by the legislature of the importance of the circular. By examining the takeover bid regulations of the Province of Ontario, which are essentially similar to those in the ISA, the article highlights the lacunae in the Nigerian law so future reform of the law may take them into account. It concludes that, unless the information contents of the directors' circular are redesigned, shareholders will continue to suffer from avoidable information asymmetry, which may add to the transaction cost of exit.

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 imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.082
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.082
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.003
Scholarly communication0.0040.005
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCommon Law World ReviewSame topicCorporate Insolvency and GovernanceFrench-language works237,207