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Record W4399041427 · doi:10.29173/mlj805

Commercial Law in Manitoba: Pre-Incorporation Transactions - Part I

2012· article· en· W4399041427 on OpenAlexaffabout
Darcy L. MacPherson

Bibliographic record

VenueManitoba Law Journal · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsLawHistoryPolitical science

Abstract

fetched live from OpenAlex

his is the first in what I hope may become a series of short articles suggesting law reform in Manitoba with respect to corporate and commercial law topics.In this first paper, my suggestion for law reform is simple: amend s. 14 of the Corporations Act 1 to cover oral pre-incorporation transactions as well as written ones. 2 While some of the arguments presented below are quite technical in nature, the basic thrust of the argument is this.Currently, the law treats written preincorporation transactions by statute and excludes oral transactions, which are left to the common law to resolve.While corporations can be formed very quickly, pre-incorporation transactions nonetheless remain a key part of any business-law practice.Therefore, if this area of law can be improved, in my view, an attempt should be made to do so.Some justifications for the maintenance of the status quo are conceivable.However, in my view, none of these justifications stand up terribly well to genuine scrutiny.The statute is very certain in its application and tends to keep transactions enforceable.This is consonant with both the purpose of the statute as facilitative of transactions, and with the general law of contracts, which generally tries to accord with the reasonable expectations of the parties.The common law is not as successful in either facilitating transactions, or in meeting the reasonable expectations of the parties.The statutory approach is also more consistent with both the general law of contracts, as well as corporate law.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.988

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 studies0.0010.000
Scholarly communication0.0000.003
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 head0.053
GPT teacher head0.247
Teacher spread0.194 · 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 designTheoretical or conceptual
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
Published2012
Admission routes2
Has abstractyes

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