METHODOLOGICAL BASES OF ASSESSMENT OF THE LEVEL OF DEVELOPMENT OF THE WORLD BUSINESS ENVIRONMENT: GLOBAL AND REGIONAL VIEW
Bibliographic record
Abstract
An attempt to offer a methodology for analyzing the business environment as in a global context (of the world) as in regional level (of Ukraine), which would come from an empirical base provided by domestic world analytical organizations of the public and private sectors of the economy was made. Methodology. As part of the methodology of assessment of the level of development of the world business environment, the author conducted a preliminary analysis of twenty-five indices examined their constituents in order to avoid duplication of index components which would lead to a distortion of research results. As part of the regional methodology, an 8-stage algorithm was proposed for assessing the level of development of the business environment of the regions of Ukraine based on 40 selected and reasonable indicators that cover 7 main areas and reflect various aspects of the business environment. Results. Nine proposed indices that are in complex, its component composition, are not overlapping and complementary, so reflect and provide a quantitative description of each of the multiple facets of this phenomenon as the world business environment. An integral estimation of the business environment of Ukraine in a regional context has been carried out; an integral index of the level of development of the business environment of a region has been calculated; both of them can act as the objective quantitative criteria for the formation of regional clusters. Objective characteristics of the business climate of a certain territory (in our case, region or group of regions) are obtained. Practical implications. Following the proposed method, the analysis of the level of development of the business environment of 70 countries in the classification limits introduced by the author in previous studies (European, North American, Latin American, African, Far Eastern, Islamic, Indian, ocean), in order to further clustering and graphical interpretation of the results. The group of leaders is formed by countries that relate to different models of the business environment. The countries of the European model are Germany, Great Britain, Sweden, the Netherlands and Austria. North American model: Canada and the United States of America. Representative of the Far Eastern model is Japan, as well as Australia which belongs to other models. The group of outsiders include countries that have a poor integrated index of business environment development, they are representatives of the African (Angola, Congo, Chad), Islamic (Syria, Somalia, Sudan) and Island (Polynesia) models. The results of analyzing of the business environment of the regions of Ukraine show that in 2017 Lviv, Kyiv region became a cluster of high development. The index of development the business environment, calculated by us, proves the extremely expressed polarization and unevenness of the processes of development of territories. Value/originality. The results obtained with graphical and formulaic interpretation make it possible to understand the сondition, problems, prospects of their overcoming, to outline directions of further development and opportunities to support the business environment in the regional and global context.
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 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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".