Bibliographic record
Abstract
The article examines the opportunities of business tendency surveys of enterprises in condition of official statistics imperfection considering the specifics of the development of Russian economy in recent years. Based on the 25-year experience of the Gaidar Institute for Economic Policy in the field of conducting and developing monthly surveys of industrial enterprises, it is shown that this data source is able to supplement the results of traditional statistics, and in the case of 2008-2009 crises, and 2015-2016 - significantly surpass them. So, the crisis of 2008-2009 was registered by IEP surveys a month before its official recognition, and especially in 2015-2016 - the absence of a crisis recession in demand, output and employment, with confident control of enterprises for finished products - were already shown in the first quarter of 2015. The flexible organization of the business surveys of the IEP allows one to directly measure many of the actual phenomena that are relevant at the present stage, but which are not available to traditional statistics. In years 2012-2017, the results of IEP surveys showed a low demand for devaluation of the ruble as a protective measure for the domestic producer in the context of a critical dependence of the industry on imported equipment, components and raw materials. For this reason, the strengthening of the ruble in February 2015 was considered by Russian enterprises to be the most important anti-crisis measure and theassessment of the actual impact of the ruble devaluation on demand, costs and investments in 2015-2017 were always negative.
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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 | 0.041 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".