Analysis of economic development of Ukraine regions based on taxonomy method
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.
Bibliographic record
Abstract
The purpose of the article is to assess the impact of the investment and innovation components on the economic development of Ukraine regions using the taxonomy method. The proposed method allows determining the object state level in a general set of objects, and ordering the objects according to growth or decrease of the indicators. The article substantiates the necessity to assess the dynamics of the regional economic development level in order to identify the reserves and capacities for increasing profitability and efficiency of further activities. The integral indicator of the regions' development level is calculated. The comparative analysis is carried out within the framework of the proposed model. The research results suggest that the proposed model enables us to trace the causes of the negative phenomena occurrence in the activities of the regions, to prevent their occurrence, and to develop complex managerial influences to provide the state with a vector of positive development.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it