A Case Study on Micro Social Network Structure of Building Industrialization: Based on Structural Hole Theory
Bibliographic record
Abstract
The industrial chain of building industrialization (BI) has been forming in China during the latest three decades development, which gradually presents a trend of networking. However, the enterprises’ implementations of building industrialization are far from satisfactory. Both practitioners and managers hold the same confusions: Who is controlling effective information resources by occupying critical path in BI network? Who decides the flow direction of materials resources in the network? To solve these doubts, this paper makes an analysis of building industrialization micro social network based on the structural hole theory. A typical industrialized construction project in Shenzhen (China) was selected for the empirical study. Firstly, a questionnaire survey is conducted to collect authentic data and Ucinet is used to delve structural holes by four indicators named effective size, efficiency, constraint and hierarchy. Secondly, the roles and function of stakeholders would be re-explained by the theory of brokerage roles. The outcomes of social network analysis indicate that developer is the information hinge of this BI project due to its largest value of effective size as well as lowest constraint. From the perspective of resource control, contractor and component supplier also occupy critical structural holes and play important roles in building industrialization network. But to some extent, the network of BI in China is not optimized. Thus, enterprises should try to adopt some reasonable accretive measures according to the market condition and self-position.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".