Bibliographic record
Abstract
Import substitution in the Russian industry shows obvious signs of a slowdown. Both comparative results of actually implemented import substitution quarter-on-quarter and plans of enterprises for the last quarter of the current year attest to this. At the same time, import substitution of machines and equipment was at a higher rate than import substi tution of industrial inputs. It is true that the Russian machine building industry does not reduce procurements of imported equipment. The food processing industry is losing momentum in import substitution of inputs either having disillusioned in the domestic raw material base or having exhausted its potential. Signifi cant part of the Russian industry pursues a policy of “import preservation” (in other words, does not reduce the share of imports) or even goes to “import expansion”. The latest assessment of the actual import substitution has been obtained for Q3 and forecast one for Q4 2015. Herewith, estimates of import substitution regarding industrial inputs was done separately from import substitution of machines and equipment. According to the obtained results for Q3 2015 one can make a rather definitive general conclusion: the Russian industry has reduced the scale of import substitution. This refers to all indicators: inputs, equipment, actual changes and plans for Q4 2015. Let us conduct an in-depth analysis of import substitution taking into account comparable results for Q2 2015.1
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".