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Record W2028277606 · doi:10.1111/etap.12123

Dead Money: Inheritance Law and the Longevity of Family Firms

2014· article· en· W2028277606 on OpenAlexafffund
Michael Carney, Éric Gedajlovic, Vanessa M. Strike

Bibliographic record

VenueEntrepreneurship Theory and Practice · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsSimon Fraser UniversityConcordia University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInheritance (genetic algorithm)VitalityLongevityBusinessEstate planningChinese familyProperty (philosophy)EconomicsLaw and economicsLawFinancePolitical scienceEstate

Abstract

fetched live from OpenAlex

“Dead money” refers to the potential for the division, reduction, and misallocation of family firm assets during intergenerational wealth transfers. We consider the effects of inheritance law provisions on property transfers and the potential impact on family firm vitality in four jurisdictions: Germany, France, Hong Kong SAR, and the United States. These jurisdictions have divergent legal origins and inheritance law regimes that generate distinct patterns of transformation and continuity in family firms. The contribution of the paper is to identify external institutional factors that determine the central tendencies on family firm longevity in a literature that has hitherto focused on internal factors such as the efficacy of adopting professional management and succession planning.

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)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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

Citations80
Published2014
Admission routes2
Has abstractyes

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