Financial Solvency of Russian Regions in 2010-2014: Continued Classification Analysis
Bibliographic record
Abstract
This article is a continuation of the first work done on the State task of the Financial University of 2013 [1-3], in which the classification of the regions of the Russian Federation according to the state statistics for 2005-2011 was proposed. Over the past period, the relevance of the issues has not decreased, and Russia’s gap in the number of subjects of the federation from the nearest “pursuers” (USA, Brazil, Germany, Canada) with the addition of the Crimea has only increased. Classification analysis of Russian regions remains a time-consuming task, as each region is unique in something and it is difficult to find something in common. We continued the analysis of the period data from 2005 to 2011 (before the crisis, the crisis and the initial stage of recovery), and also supplemented it with data from 2012-2014 about a new crisis. We wanted to advance in obtaining a tool for monitoring the financial viability of the regions and to test it in real and changing conditions. Work is continued also to assess the quality of statistical data contained in the Rosstat database, which precedes the classification analysis itself.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".