The Development of the Logistics System of Kazakhstan as a Factor in Increasing its Competitiveness
Bibliographic record
Abstract
Efficient logistics system is an important factor for stable economic growth of the state. Rational use of transport and logistics capabilities of the country stimulates the rapid development of related industries and sectors of the economy. In a globalizing world economy and the expansion of integration processes with the introduction of the Eurasian Economic Union, Kazakhstan is implementing an ambitious strategic goal of building a competitive economy. In this context, a key role in achieving these goals must go to efficient transport and logistics system, which should provide not only a high and efficient transport connectivity in the country, but also the necessary level of integration of Kazakhstan into the global transport and logistics network. And in today's Kazakhstan, the level of logistics costs in the manufacturing complex is one of the highest in the world, the share of logistics costs in the final cost of production is approximately 20-25%. In this case, the global average is 11%, in China - 14% in the EU - 11% in the US and Canada - 10%. At present, the lack of efficiency of the transport system of Kazakhstan is a brake on the development of the economy as a whole. In this regard, there is a question of logistics research in Kazakhstan and its impact on the country's economic growth. This article discusses the problems and obstacles to the development of the logistics system of Kazakhstan and ways of their solutions, the analysis of the logistic capacity of the country.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".