The Republic of Tatarstan in the conditions of 2000s economic crises: strategies and results of the measures taken by the local government
Bibliographic record
Abstract
The article assigns an objective to identify the particular aspects of the actions taken by the government of the Republic of Tatarstan to solve the problems of the socio-economic creses of the 2000s. A special attention is given to the analysis of the current situation in the region in the context of the COVID-19 pandemic and some acute phenomena in the republic's economy in the second quarter of 2020. At the time under discussion, society showed demand in quick and timely socio-economic support from the state. A package of federal measures is being adopted to render assistance to various market participants. The Center provided opportunities for the regions to make decisions based on the local situation, which allowed them to take independent steps in this direction. Such mechanism was not applied during the crises of 2008 and 2015, while the measures of that time were not as transparent as today. To compare the strategies of the Tatarstan Government aimed at stabilizing the socio-economic situation in the republic in the context of the three crises, the article refers to documents for the period of 2009–2020, which are publicly available on the website of the Ministry of Economy of the Republic, and the data of the socio-economic situation in Tatarstan from the Federal State Statistics Service for 2019–2020.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".