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Record W2581974275 · doi:10.5465/annals.2014.0053

Introducing the Family: A Review of Family Science with Implications for Management Research

2017· review· en· W2581974275 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAcademy of Management Annals · 2017
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsLeverage (statistics)SociologyBehavioural sciencesFamily businessPublic relationsPsychologyKnowledge managementSocial scienceManagementPolitical science

Abstract

fetched live from OpenAlex

While families have a large and undeniable impact on human behavior, management research is yet to fully embrace how aspects of families (e.g., family-member relationships, family structures, and family events) influence entrepreneurs, employees, managers, and their organizations. There is a large body of research known as family science that draws from sociology, psychology, and education and offers theories that describe families and explains important family outcomes. Management researchers have not widely exploited knowledge from family science, but it could be applied to advance management theories by answering questions about how families impact organizations and the people in them. Therefore, we review seven family science theories and leverage our review to map research agendas for how management researchers might use each theory to advance understanding of how families influence organizations and vice versa. Our review suggests a wide range of research topics in management that could advance by drawing upon family science research.

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.

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.810
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0040.002
Research integrity0.0000.001
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 head0.416
GPT teacher head0.495
Teacher spread0.079 · 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