Identifying configurations of multiple co‐located clusters by analyzing within‐ and between‐cluster linkages
Bibliographic record
Abstract
Abstract The Beijing economy has an unusual industrial configuration consisting of multiple industry parks in the biomedical industry with a cluster‐like structure, specifically Yizhuang Park, Daxing Park, and ZLS (Zhongguancun Life Science) Park. If these industry parks can indeed be conceptualized as clusters, a number of questions arise regarding their collaborative or competitive relationships that can potentially be both beneficial and detrimental. We begin analyzing this case of three biomedical industry clusters by conceptualizing four ideal‐type scenarios of co‐located cluster configurations and identifying their within‐cluster and between‐cluster linkage patterns. Based on a relational research design, we develop a simple testing procedure that allows us to identify the specific empirical cluster configuration at hand. Based on a survey of labor market, government event, research, and production linkages of 164 firms in the three biomedical industry parks, we conduct statistical tests and conclude that Beijing represents a case of three collaborating clusters, with some elements of integration.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".