Social Network Analysis of Stakeholder Relationships during Construction Industrialization in China
Bibliographic record
Abstract
Modular and off-site construction has garnered world-wide attention, as evidenced by the gathering momentum of construction industrialization in China. This entails a transition for stakeholders within the construction industry, as construction industrialization will alter relationships among stakeholders on a number of levels. In this research, the stakeholders involved in construction industrialization in China are identified, and the stakeholder relationships are quantified through three surveys. Based on the collected data, the stakeholder relationships are mapped and analyzed using social network analysis. The results indicate that the social network of industrialization construction is not dense and less tied, generates relatively low impacts on the behaviors of individual stakeholders; as the most influential stakeholders, the general contractor, owner, and surveyors and designers control most of the resources; due to their influence in the form of policies, the government can become an important stakeholder during the process of construction industrialization Through strengthening the policy guidance, it can attract new stakeholders for the integrated network, incubate a construction industrialization corporate group with integrated abilities of R&D, design, construction and operation, which is valuable to effectively promote the development of construction industrialization in China.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".