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Record W3124079543

Been There, Done That: The History of Corporate Ownership in Japan

2004· preprint· en· W3124079543 on OpenAlexaboutno aff
Randall Mørck, Masao Nakamura

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2004
Typepreprint
Languageen
FieldSocial Sciences
TopicJapanese History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsKeiretsuPoliticsBusinessGovernment (linguistics)Stock (firearms)Corporate structureControl (management)Market economyCorporate governanceEconomyEconomicsPolitical scienceFinanceManagementLawGeography
DOInot available

Abstract

fetched live from OpenAlex

Japan's corporate sector has, at different times in recent history, been organized according to every major model. Prior to World War II, wealth Japanese families locked in their control over large corporations by organizing them into pyramidal groups, called zaibatsu, similar to structures currently found in Canada, France, Korea, Italy, and Sweden. In the 1930s, the military government imposed a centrally planned command economy, with private property rights retained as little more than a legal fiction. The American occupation force replaced this with a widely held corporate sector similar to that of the United Kingdom and United States. A bout of takeovers and greenmail ensued. To defend their positions, Japanese top executives placed small numerous blocks of stock with each others' firms, creating dense networks of small intercorporate blocks that summed to majority blocks in each firm. These networks, called keiretsu, halted hostile takeovers completely. Although their primary functions were to lock in corporate control rights, both zaibatsu and keiretsu were probably also rational responses to a variety of institutional failings. Successful zaibatsu and keiretsu were enthusiastic political rent-seekers, raising the possibility that large corporate groups are better at influencing government than free standing firms. In the case of keiretsu especially, this rent seeking probably retarded financial development and created long-term economic problems.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0050.006
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.085
GPT teacher head0.279
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 source (direct Gemma or distilled Codex), 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

Citations9
Published2004
Admission routes1
Has abstractyes

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