Adverse retention of family talent in intrafamily succession
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.
Bibliographic record
Abstract
In both a practical and theoretical sense, management succession is one of the most important issues facing family firms because intentions for it influence behavior and the ability to execute it successfully ultimately influences long-term survival. One of the greatest challenges in family firms with intention for intrafamily management succession is to ensure that the most talented family members stay in the firm. Thus, this paper deals with the problem of adverse retention, a situation wherein the more talented family members leave but the less talented family members stay. We use classical microeconomic-labor-supply arguments to explore five scenarios of increasing complexity to illustrate how personal attributes, pecuniary and nonpecuniary benefits, relationships between family members, and interactions with the external labor market can give rise to or prevent adverse retention. We discuss implications and research directions suggested by our application of the model to the adverse-retention problem.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it