Family business research: The next generation
Bibliographic record
Abstract
The first quarter of the 21st century has seen significant advancements in family business scholarship. While many scholars have sought to develop a singular “theory of the family firm,” most research has instead focused on understanding how salient management and entrepreneurship theories operate within family firms, thereby enriching our knowledge of the heterogeneity of this business type. This introduction to the special collection of papers from the 2024 Theories of Family Enterprise Conference integrates and expands multiple theoretical frameworks examined within family firms, reviews and synthesizes their contributions, and extends their implications for the broader family firm literature, while also offering multiple directions for future research. Further, the seven articles in this special collection help to further unpack the heterogeneity of this business type and shed light on the advances being made from a theoretical perspective for understanding family businesses in this next generation of scholarly inquiry.
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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 | 0.025 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
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".