Structural Challenges for SOEs in Belarus : A Case Study of the Machine Building Sector
Bibliographic record
Abstract
Are Belarus's state owned enterprises positioned to grow in 2011-2015 as successfully as in 1995-2006? State owned enterprises account for 55 percent of Belarus's output and two-thirds of overall employment; economic growth in 1995-2006 was the result of capacity expansion and productivity improvements in state owned enterprises. These sources of economic growth originated in policy decisions that preserved the functioning of the command and control economy and allowed the country to exploit preferential commercial access to the Russian market in several goods and services. Are the same reasons likely to facilitate the performance of state owned enterprises and overall economic growth in 2011-2015? This paper concludes that this is not likely to happen. Times have changed: the slowdown in production and exports in 2009-2010 was unquestionably associated with a transitory decline in demand for durable goods in Russia. But there have also been more permanent market forces at work: a steady increase in competition in Russia and other Commonwealth of Independent States markets resulting from low-price Chinese and Russian-produced capital goods; and a shift in demand from low-quality/low price to high-quality, high-price transport equipment demand in Russia and other Commonwealth of Independent States markets. And these forces are there to stay. This conclusion leads to the following questions: Would state owned enterprises be able to adapt to observed market changes? What reforms would be relevant to facilitate the necessary adaptation?
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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 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".