Bibliometric evaluation of research on political risks in construction projects
Bibliographic record
Abstract
The current study aims to provide an overview of the research on political risk using the Web of Science（WoS）database as well as summarize research results and put forward some suggestions for research directions of political risk in international construction projects. It is the first time scientometric analysis of political risk research is executed. In this regard, the papers related to political risk in the WoS database have been retrieved and the literature is sorted out by visual and content analysis methods. Visual analysis is used to analyze the research overview, knowledge base, and research hotspots of this field. The content analysis method is adopted to expound the current research focus from three perspectives inducing the influence of political risk, risk assessment, and risk management measures. The results show that in the political risk context, the number of publications has experienced an increasing trend in recent years. Based on the existing literature on political risk for all companies, this overview provides some suggestions to address the political risk in international construction projects in the future. The results contribute to the scholars understanding of the research overview, research hotspots, and future research directions of political risk research in construction projects.
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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 | 0.017 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.218 | 0.263 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".