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Record W1412100280 · doi:10.1017/cbo9780511813481.005

Types of company directors and officers

2005· book-chapter· en· W1412100280 on OpenAlexaboutno aff
Jéan Du Plessis, James McConvill, Mirko Bagaric

Bibliographic record

VenueCambridge University Press eBooks · 2005
Typebook-chapter
Languageen
FieldSocial Sciences
TopicAustralian History and Society
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessManagementAccountingPsychologyEconomics

Abstract

fetched live from OpenAlex

The key directors on our board all know what I have done to make our company perform. They made me the CEO because I was the best candidate they could find. I have worked my butt off at great sacrifice to my family and personal life to transform this company and make it perform better than it ever had before. I don't need any of their penetrating questions or second-guessing. Thanks to my own tough bargaining, I am financially secure and set for life. If they can get someone better than me to do the job, then that's what they should do. Until then let them back off and stay out of my way. David SR Leighton and Donald H Thain, Making Boards Work , Whitby, Ontario, McGraw-Hill Ryerson Ltd, (1997) 6 (quote from an anonymous sceptical Canadian CEO). We trained hard – but every time we were beginning to form up into teams, we would be reorganised. I was to learn later in life that we tend to meet any new situation by reorganising, and [what] a wonderful method it can be for creating the illusion of progress while producing confusion, inefficiency and demoralisation. The famous words of Roman writer Gaius Petronius: Petronii Arbitri Satyricon , AD 66 as quoted by Nigel Kendall and Arthur Kendall, Real-World Corporate Governance , London, Pitman Publishing (1998) 212. Definition of ‘director’ De jure and de facto directors covered The corporations laws of most common law jurisdictions contain a definition of ‘director’.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.100
Threshold uncertainty score0.336

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0060.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1000.037

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 head0.032
GPT teacher head0.229
Teacher spread0.197 · 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 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
Published2005
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooksSame topicAustralian History and SocietyFrench-language works237,207