Management of Adaptation of Organizational and Economic Mechanisms of Construction to Increasing Impact of Digital Technologies on the National Economy
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".