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Record W2096279024 · doi:10.1177/0894486511421487

Advancing Family Business Research Through Narrative Analysis

2011· article· en· W2096279024 on OpenAlex
Alexandra Dawson, Daniel Hjorth

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

VenueFamily Business Review · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFamily Business Performance and Succession
Canadian institutionsConcordia University
Fundersnot available
KeywordsFamily businessNarrativeStyle (visual arts)Identity (music)Narrative inquiryEcological successionSociologyField (mathematics)Social identity theoryPerspective (graphical)Public relationsPolitical scienceBusinessComputer scienceSocial scienceMarketingSocial groupHistoryAesthetics

Abstract

fetched live from OpenAlex

Despite advances in family business research, the field would benefit from greater methodological rigor. However, rigor does not mean convergence of methodologies. In this article, the authors adopt a novel approach, based on narrative analysis, to address the succession process in a family business. This interpretive perspective is appropriate for family business studies, which address multifaceted and complex social constructs that are performed by different actors in multiple contexts. The analysis highlights five key themes centering on leadership style and succession, trust and communication, balance between agents, history and identity, and fear of losing one’s identity and social standing through the succession process.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.453
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.029
Science and technology studies0.0010.000
Scholarly communication0.0000.007
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.113
GPT teacher head0.337
Teacher spread0.225 · 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