An Empirical Inquiry into 'Academy-Run Enterprises' in China: Unique Characteristics and Evolutionary Changes
Bibliographic record
Abstract
This study analyzes the academy-run enterprises (AREs) in China, which have played an important role in the development of high-tech industries in China, but have rarely been deeply addressed in previous studies. The AREs, despite ostensibly sharing some similarities with the academic spin-offs (ASOs) found in other countries, have distinct historical origins and characteristics in China. In this study, we try to clarify the distinct features of Chinese AREs, particularly in terms of their relationship with their mother institutions, using questionnaire survey data collected from 102 AREs and subsequent follow-up interviews with the ARE managers. This study finds that, while the AREs have enjoyed an exclusive right to exploit various assets of their mother institutions, they have suffered from the interventions of the mother institutions and ambiguous property right arrangements with the mother institutions. More recently, AREs have begun to evolve in response to the changing environment. Furthermore, recently initiated reform measures are expected to accelerate this evolution. Using the survey results, this study assesses the short-term and long-term impact of the reform on Chinese AREs, and the subsequent impact on the academia-industry relationship and the national innovation system (NIS) in China.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".