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Record W2616363301 · doi:10.4018/ijcfa.2015070103

The Impact of the Recent Economic Crisis in the Construction Sectors of the South-European Economies

2015· article· en· W2616363301 on OpenAlexaff
Efthimios Nikolakopoulos, Nikolaos Karaliotas, Efstathios Benetatos

Bibliographic record

VenueInternational Journal of Corporate Finance and Accounting · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicEfficiency Analysis Using DEA
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScope (computer science)Order (exchange)EconomyEconomicsEconomic sectorBusinessMacroeconomicsFinance

Abstract

fetched live from OpenAlex

The scope of this paper is to investigate the performance of listed companies of the construction sector in the southern European member state of the E.U., namely Greece, Italy, Portugal and Spain, taking into consideration their economic conditions as they have been formed by the recent economic crisis. The structure of the proposed methodology is based on a non-parametric method of DEA. Initially, it is attempted to compare the listed construction companies of each country and then findings are explained in order to draw comparative conclusions of their performance on a cross country basis. By using statistical regression methods, in the second stage of investigation is attempted for possible correlation between each country's efficiency scores and a group of key macroeconomic variables which can show the possible changes in the effects of crisis between the countries under examination.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.337
Teacher spread0.262 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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

Citations2
Published2015
Admission routes1
Has abstractyes

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