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Record W2799703943 · doi:10.4337/9781784711481.00036

Canadian M & A: a comparative perspective

2016· book-chapter· en· W2799703943 on OpenAlexaboutno aff
Christopher C. Nicholls

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2016
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Governance and Law
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate lawShareholderOptimal distinctiveness theoryMergers and acquisitionsPoliticsCorporate governancePolitical sciencePerspective (graphical)Tender offerLaw and economicsLawBusinessSociologyFinance

Abstract

fetched live from OpenAlex

For many American lawyers and business people, Canada is the ‘familiar stranger’. Canadian legal and regulatory measures in the M & A field have many similarities to American models. But the many apparent similarities sometimes mask the distinctiveness of the Canadian corporate and legal landscape. From the distinctive Canadian use of the plan of arrangement to structure friendly acquisitions to the prominent and decidedly pro-shareholder role historically played by Canadian securities regulators in hostile bids, the Canadian M & A environment offers a number of illuminating examples of alternative legal and regulatory approaches to issues encountered in major acquisitions worldwide. Critical distinctions between Canadian and American takeover law have important theoretical and practical implications. The Canadian merger and acquisition environment has become of considerable interest to US business executives looking for suitable acquisition targets as well as to activist investors. From a broader theoretical or policy perspective, the Canadian system provides a useful and illuminating counterpoint to the American M & A regime. Canadian and U.S. legal, economic and political systems are sufficiently similar to make comparisons meaningful. Yet, key points of distinction between Canadian and American merger and acquisition law and practice make comparisons interesting. The Canadian law governing mergers and acquisitions today comprises a complex modern array of federal and provincial corporate and securities law provisions blending English, American, and uniquely Canadian features.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0040.003
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.004

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 head0.042
GPT teacher head0.230
Teacher spread0.187 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2016
Admission routes1
Has abstractyes

Explore more

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