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Record W2802053595 · doi:10.4337/9781849805735.00010

Formation of companies and the rules of capital maintenance

2014· book-chapter· en· W2802053595 on OpenAlexaboutno aff
Jiangyu Wang

Bibliographic record

VenueEdward Elgar Publishing eBooks · 2014
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
Fundersnot available
KeywordsShareholderBusinessGovernment (linguistics)Limited liabilityLiabilityFinanceCapital (architecture)Corporate governance

Abstract

fetched live from OpenAlex

The purpose of incorporation is to create a corporate entity - with legal personality - distinct from its members. Doing Business in 2004, the first issue in the World Bank's annual reports investigating the regulations that affect business activities, points out that incorporation has four advantages. 'First, legal entities can outlive their founders. Second, resources are pulled together, as shareholders join forces in establishing the company's capital. Third, the formal introduction of limited liability … reduces the risks of doing business … Fourth, registered businesses have access to services - provided by public courts or private commercial banks - that are not available to unregistered firms. 'Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2002) examined regulation of entry for start-up firms in 85 countries. The authors focused on procedures, and the time and cost that a start-up must spend before it could operate legally. They observed that: Countries differ significantly in the way in which they regulate the entry of new businesses. To meet government requirements for starting to operate a business in Mozambique, an entrepreneur must complete 19 procedures taking at least 149 business days and pay US$256 in fees. To do the same, an entrepreneur in Italy needs to follow 16 different procedures, pay US$3946 in fees, and wait at least 62 business days acquire the necessary permits. In contrast, an entrepreneur in Canada can finish the process in two days by paying US$280 in fees and completing only two procedures.

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: Other · Consensus signal: none
Teacher disagreement score0.937
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.016
GPT teacher head0.190
Teacher spread0.174 · 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
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
Published2014
Admission routes1
Has abstractyes

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