Декомпозиция и совместный анализ циклов роста в динамике индикатора экономического настроения и индекса физического объема валового внутреннего продукта
Bibliographic record
Abstract
The article considers potential of composite indicators of business tendencies surveys to measure short-term cycles of economic dynamics and presents the main results of the empirical study of the Economic Sentiment Indicator (ESI HSE), combining key information on sectoral surveys business trends in real, consumer and service sectors of the Russian economy for the I quarter 1998 - I quarter 2014. The main goal of the study is an answer to questions about admissibility to use business tendencies monitoring results in composition of the national information infrastructure. Do, for example, short-term trends of economic sentiment reflect the basic trajectory of real economy development for the period under review? Is it possible to use ESI HSE time series as an indicator of phases and turning points of growth cycles in the dynamics of statistical macro-aggregates, namely, volume index of the Gross Domestic Product? Key findings indicate scientifically valid ability of business tendencies monitoring indicators to be a part of the national corpus of short-term cyclical indicators and statistically significant admissibility of their practical usage in this capacity. This expands considerably information capabilities of business tendencies monitoring in structural-spatial analysis of entrepreneurial behavior and in identification of cyclical development of the economy of the country in temporal aspect.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".