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Record W3171150266 · doi:10.5267/j.jpm.2021.5.003

Bibliometric evaluation of research on political risks in construction projects

2021· article· en· W3171150266 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Project Management · 2021
Typearticle
Languageen
FieldEngineering
TopicInfrastructure Resilience and Vulnerability Analysis
Canadian institutionsnot available
FundersChinese Academy of EngineeringNational Science Foundation
KeywordsPolitical riskPoliticsContext (archaeology)Risk managementPolitical scienceContent analysisData scienceRisk analysis (engineering)BusinessComputer scienceSociologySocial scienceGeographyFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0270.032
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.185
GPT teacher head0.454
Teacher spread0.269 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it