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Record W2886772827 · doi:10.1177/1042258718792290

Social Structures, Social Relationships, and Family Firms

2018· article· en· W2886772827 on OpenAlexaff
Thomas Zellweger, James J. Chrisman, Jess H. Chua, Lloyd Steier

Bibliographic record

VenueEntrepreneurship Theory and Practice · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersInstitute for Small Business and Entrepreneurship
KeywordsCorporate governanceFamily businessAffect (linguistics)Social exchange theorySociologyTheme (computing)Social structureBusinessPublic relationsEconomic geographyMarketingPolitical scienceSocial scienceEconomicsComputer science

Abstract

fetched live from OpenAlex

In this introduction, we observe that the study of social structures and social relationships constitutes a common theme among the articles and commentaries contained within this special issue on Theories of Family Enterprise. Individuals and organizations are embedded in complex networks of social organization and exchange. Within business enterprises, familial relationships engender unique goals, governance structures, resources, and outcomes. We discuss these relationships, potential research directions, and the contributions made by the articles and commentaries. In so doing, we expand the literature on how social structures and social relationships affect the behavior and performance of family firms.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.607
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.043
GPT teacher head0.291
Teacher spread0.248 · 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.

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

Citations119
Published2018
Admission routes1
Has abstractyes

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