Russia Economic Report, November 2017, No. 38: Russia’s Recovery—How Strong Are Its Shoots?
Bibliographic record
Abstract
Global growth gained momentum in 2017. After slowing to 2.4 percent in 2016 as investment and trade weakened, global growth accelerated to a projected 2.7 percent for 2017.Moreover, the recovery has been broad-based.Global trade also continued to strengthen and external financing conditions remain benign.Amid these positive tailwinds, along with firming oil prices and growing macro-stability, the Russian economy returned to modest growth in 2017. The growth momentum of the second half of 2016 spilled over to 2017 and was especially strong in the second quarter this was supported by a rebound in domestic demand in the first half of 2017—which also contributed to a growth slowdown starting in the third quarter.On the production side, mineral resource extraction, transportation, and state management and provisioning for national security drove growth in the first quarter of 2017.Monetary policy remained prudent and consistent with the inflation-targeting framework. However, improvement in headline indicators masks underlying disparities and remaining vulnerabilities.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.018 |
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".