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Record W3125312294

The Allocation of Profits between Related Entities and the Oppression Remedy: An Analysis of Ford Motor Co. V. Omers

2004· article· en· W3125312294 on OpenAlexaboutno aff
Kim Brooks, Anita Anand

Bibliographic record

VenueeYLS (Yale Law School) · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTaxation and Legal Issues
Canadian institutionsnot available
Fundersnot available
KeywordsOppressionLaw and economicsShareholderLawEconomicsSociologyPolitical scienceManagementCorporate governancePolitics
DOInot available

Abstract

fetched live from OpenAlex

In Ford Motor Co. v. Ontario Municipal Employees Retirement Board, the Ontario Superior Court of Justice reviewed the transfer pricing arrangements between parent and subsidiaries Ford US and Ford Canada in the context of a going-private transaction. Its review was the key to resolving the two main issues in the case: first, did the transfer-pricing arrangements understate Ford Canada's profits so as to undermine the fair value of Ford Canada's shares? And second, did the transfer-pricing arrangement oppress or unduly disregard the interests of Ford Canada's minority shareholders so as to give rise to the oppression remedy? In this comment, the authors analyse the Court's reasoning and its implications for tax and corporate law. They review profit allocation methods that were available to the Court (and to Ford US) and the Court's rationale in adopting the profit split method. Although the authors agree with the Court's reasoning regarding the profit allocation methods, they argue that the reasoning with regard to oppression gives rise to some important questions regarding the proper analysis for oppression when board conduct is impugned. They disagree with the Court's use of reasonable foreseeablity as a basis for assessing whether the oppression remedy should be granted and argue that where board conduct is at issue in an oppression action, courts must consider whether directors have breached their fiduciary duties.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.837
Threshold uncertainty score0.944

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.253
Teacher spread0.238 · 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.

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

Citations1
Published2004
Admission routes1
Has abstractyes

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