Bibliographic record
Abstract
The article considers the importance of e-commerce in modern market conditions. E-commerce is a huge part of the whole economy and is vital for businesses that sell their products or services online. It gives businesses the ability to reach more customers than traditional retailers. Because so many people shop online, this is the fastest retail market. The main purpose of Internet marketing is revealed. Due to the high bandwidth of the information flow, the system (Internet) is the main link in the exchange of goods, services and information today. Almost all businesses in the world are focused on improving Internet connections to increase the efficiency of business. For many entrepreneurs, the Internet is the main way to spread information about their products and invitations to cooperate. The place and role of e-commerce as an effective marketing tool are substantiated. The leading countries that have achieved the greatest success in the field of e-commerce include: China, the United States, the United Kingdom, Japan, Germany, France, South Korea, Canada, Brazil. According to various estimates, about 70% of the population of Ukraine uses the Internet. A year ago, this figure was 60%, ie during the pandemic, the level of Internet use increased significantly. Almost 33% of the adult population regularly shop online. This audience is growing gradually, but it is important to note that we are talking about regular online purchases. Virtually all Internet users have already had the experience of at least some online shopping. The COVID-19 pandemic has shaken economic markets and dramatically changed the daily lives of many people and companies around the world. It is difficult to find any aspect of human life that would not suffer, the corona crisis has changed the world of e-commerce. As millions of people stayed home in early 2020 to curb the spread of the virus, digital channels have become the most popular choice of replacement for crowded stores and personal shopping. Thus, e-commerce is a global phenomenon that is growing at a healthy pace in almost every country. And in a global economy and crisis, e-commerce has become one of the most important components, a strong catalyst for economic development and an effective marketing tool in today’s volatile market conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".