Improving the Classification of Digital Marketing Tools for the Industrial Goods Promotion in the Globalization Context
Bibliographic record
Abstract
In today's environment, the use of digital marketing tools is of great importance for domestic manufacturers, as it allows them to promote their products to the world markets quickly, with a relatively small cost, to provide target audience influence, to form and enhance their own image, as well as the image of their products, etc. The constant updating of the Digital Marketing tools and the disagreement among scientists regarding the systematization and classification of the objects of the digital environment need further consideration. The authors attempt to further develop the classification of digital marketing tools in the terms of identifiing new classification attribute "By type of mediation", which contributes to further systematization of digital tools, deepening understanding of the impact of various tools (direct, indirect), the degree of their specialization and economic interest, that makes possible their effective implementation at different entities. The comparative analysis of the main digital tools is being held resulting in defining their characteristics and the peculiarities of application. It is suggested to determine the importance factor of using each digital tool to promote industrial production by the method of pairwise comparison. It is proposed to consider the importance factor of using a particular digital tool when calculating the cost-effectiveness of using it.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".