Building Our Understanding of Daughters’ Inclusion in the Family Business Succession Process
Bibliographic record
Abstract
Abstract This chapter expands our understanding of daughters’ inclusion in family business succession, analyzing why and how it can and does take place. Our work reveals that things are much more complex and diverse than research tells us in terms of daughters, their families, and their businesses. Daughters are not only “in” or “out” of the family business. They can be included in a variety of ways, at different moments, following different paths, in a diversity of contexts. Based on 10 years of qualitative research data on family business succession, we explain and discuss how gender dynamics in the family and the business systems affect succession practices and outcomes, beyond the individual level analysis. We used six selected and contrasted cases to illustrate the influence that gender, birth order, family inherited culture, business hierarchies and history, interpersonal relationships (parents-heirs-other stakeholders), as well as ownership transfers, governance rules and management procedures have on intergenerational succession, and particularly in daughters’ family business inclusion. From our findings, readers can draw practical recommendations for family business owners, managers and successors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".