Research on Chinese Family Businesses: Perspectives
Bibliographic record
Abstract
ABSTRACT This introduction traces the disappearance of Chinese family businesses from 1949 to 1978, their revival since then, and their future challenges. It then summarizes the three papers included in this Special Issue and proposes an agenda for family business studies in China. The article first focuses on the nonmarket social and political network strategies that these family-centered business organizations have had to adopt in order to overcome the difficulties they faced in accessing opportunities and resources as a result of Chinese culture's traditional low esteem for merchants and the government's continuing preference for a state-dominated economy. Family firms have so far been able to grow disproportionately rapidly in China's economy because, by leveraging the shared interests and dedication of immediate and extended family members, they have been able to achieve lower cost and higher efficiency, respond quickly to market changes, and expand social and political networks. These nonmarket strategies, however, also have a dark side. Furthermore, as the liberalization of China's economy deepens, competition must rely critically on market strategies such as innovation, alliances, and internationalization. The proposed research agenda addresses these future challenges as well as some research questions unique to Chinese family businesses.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".