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Record W2899419623 · doi:10.1139/cjce-2018-0249

Financial issues in construction companies: bibliometric analysis and trends

2018· article· en· W2899419623 on OpenAlexvenueno aff
Selin Gündeş, Nur Atakul, Faruk Buyukyoran

Bibliographic record

VenueCanadian Journal of Civil Engineering · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicConstruction Project Management and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsFinanceCapital structureIdentification (biology)ScopusProductivityBusinessEconomicsPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

The success of construction contractors largely depends on the specific terms and the availability of sufficient funds for realizing planned projects. Financial issues in construction have been discussed since mid 1970s, yet no consensus about progress has been reached in the construction finance literature. A systematic analysis of 259 finance related studies in construction is undertaken to identify research trends, critical topics, and performance of journals and authors. To map the productivity in construction finance field, Scopus database was searched for the entire period for which this database provides online coverage. Results reveal that “financial health” category, in particular one group of studies aiming to monitor and assess the financial performance of construction organizations for broader strategic issues pervaded the construction finance research. However, notably the “identification of capital structure, determinants and financing instruments” category received less and only recent attention from scholars, despite the significance of capital structure decisions under firm and country specific determinants in preventing company failures.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.996
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.1630.203
Science and technology studies0.0010.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.032
GPT teacher head0.298
Teacher spread0.265 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

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".

Quick stats

Citations25
Published2018
Admission routes1
Has abstractyes

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