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Record W3008854634 · doi:10.6000/1929-7092.2020.09.15

Management of Adaptation of Organizational and Economic Mechanisms of Construction to Increasing Impact of Digital Technologies on the National Economy

2020· article· en· W3008854634 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 Reviews on Global Economics · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Business Development Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsAdaptation (eye)Digital economyBusinessNational economyEconomic systemEconomic impact analysisEconomicsPolitical scienceMicroeconomics

Abstract

fetched live from OpenAlex

Motivation: We note significant problems of developing countries, including Ukraine, with the adaptation of organizational and economic mechanisms of the construction industry to increase impact of digital technologies on their economic systems and construction sectors.Novelty: The scientific novelty is a set of management activities for the adaptation of organizational and economic mechanisms of Ukrainian construction companies to the development of digital technologies.Methodology and Methods: The research methods used in this study are a quantitative analysis of statistics on the dynamics of world GDP, the global construction industry, the construction industries of developing countries, including Ukraine, the global digital market, digital markets in developing countries, including Ukraine, for ten years using one-dimensional statistical methods (UT) based on random sampling. The paper also uses the correlation and regression analysis, namely the linear regression model, as well as the statistical verification of the obtained model by calculating the pair correlation coefficient, the coefficient of determination and the Chaddock scale in the study of the impact of digital technology development on the Ukrainian construction industry.Data and Empirical Analysis: To conduct the study, data were collected and an empirical analysis was conducted regarding the dynamics of world GDP, the world construction industry, the construction industries of developing countries, including Ukraine, the global digital market, digital markets in developing countries, including Ukraine, in 2009-2018 according to statistics from Knoema, The World Bank, Deloitte, State Statistics Service of Ukraine, UNCTAD, International Data Corporation, Gartner.Policy Considerations: The economy of the country and the construction sector requires development and implementation of a set of management activities to adapt organizational and economic mechanisms of construction in Ukraine to development of digital technologies.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.079
Threshold uncertainty score0.506

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.049
GPT teacher head0.245
Teacher spread0.196 · 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