The Arts and Family Business: Linking Family-Related Resources to the Market Environment
Bibliographic record
Abstract
There has been a good deal of debate regarding the performance of family firms. Explanations for divergent findings have ranged from variations in governance arrangements to particular organizational practices. One avenue that has not been much explored is the role played by industry. We theorize that industry requirements, when coupled with firm-specific behaviors and resources, can account for why some family businesses do well. More precisely, due to their long term orientation and the intimate connection among family members, some family firms are especially adept at accumulating human capital, passing on tacit knowledge, protecting and leveraging reputation, and building strong relationships and slack resources. These capacities promote success in industries with high levels of uncertainty regarding product quality, product value and demand. The arts-related industries, we shall argue, are one such environment, and we use it to demonstrate our thesis that family firms, if oriented appropriately, can thrive where relationships, tacit knowledge and reputation are demanded.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".