Bibliographic record
Abstract
The trends of development of digital marketing were investigated using statistical data on Internet users in U.S., obtained from a study performed by Pew Research Center at the beginning of 2019, and in Ukraine, obtained from a study performed by the research holding Factum Group Ukraine on the initiative of International Association of Ukraine in ІІІ quarter 2018 and 2019. The data were collected by the following criteria: age, gender, education, region and finances. Data analysis for the two countries showed the same result. The technological awareness of the society increases every year, which leads to more efficient management, sales, transportation and financial services for the consumers/clients of the enterprise. Indicators also show that both children and adults, people living in the city and in the countryside, people who are financially independent and people with both middle and low income, people with higher education and professional primary education, they all use the Internet. For communication, making purchases, doing research etc. Society has gone digital and businesses need to adapt by changing their management practices. Marketers are creating ways to promote businesses by leveraging new technology. Marketing plays a key role in the digital revitalization of any enterprise. It is through digital marketing that consumers and businesses learn about certain events (legal, economic, social, religious, etc.), and not only are they being informed, they can also inform others. Mobile devices, the Internet, local area networks, digital television and other media can also be used to collect information and conduct marketing research.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".