Spatio‐temporal dynamics of technical efficiency in China’s specialized markets: A stochastic frontier analysis approach
Bibliographic record
Abstract
Abstract China’s specialized markets as a special form of bottom‐up capital agglomeration have played a key role in fostering regional development. It once exhibited positive externalities with high efficiencies. However, given the rapid proliferation of specialized markets and the penetration of E‐commerce, their advantages may have shifted and the understanding of this shift is limited. The paper explores the spatio‐temporal dynamics of China’s specialized markets in terms of technical efficiency. Based on turnover data from Statistical Yearbooks of China Commodity Exchange Market from 2000 to 2016, technical efficiencies in specialized markets are measured by a Stochastic Frontier Analysis (SFA) approach using panel data. The results show that (a) the technical efficiencies in China’s specialized markets are significantly divergent in space over time; (b) labor input has notable effect on efficiency increase, while capital input has no significant effect; (c) informatization level, cluster size, and degree of market openness are identified to have a positive effect on specialized market’s technical efficiency. This paper argues that specialized markets should be taken seriously in the cluster evolution research. The role of proximity and the bounded links between specialized markets and their local clusters is the key to understanding their changing forms, performances, and trajectories.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".