E-COMMERCE DEVELOPMENT IN RUSSIA: TRENDS AND PROSPECTS
Bibliographic record
Abstract
Russian commercial enterprises operate in conditions of increased competition and global effects of the world economic crisis. Limited financial resources, instability of the economic environment, escalating demands of consumers for the quality of services make it necessary to search for additional sales channels. With the popularization of the Internet and integrated automation of economic sectors, the role of e-commerce is becoming increasingly important. In 2015, the B2C e-commerce turnover in Russia increased by 6.6% compared to the previous year and amounted to 21.621 million euros. Russia is ranked first in Europe in terms of the number of e-shoppers (30 million people in 2015), and this number keeps on growing steadily. The main purpose of this paper is to analyze the obstacles to and the opportunities for the development of the Russian e-commerce market. High practical importance and lack of theoretical basis underlie the relevance of the article’s subject.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".