ТЕХНОЛОГІЯ УПРАВЛІННЯ ЗНАННЯМИ ПРО ВІРТУАЛЬНЕ ПРОСУВАННЯ
Bibliographic record
Abstract
The article presents a new concept of “Management of knowledge about virtual promotion” on the Internet. Usually a real produ ct or service is being divided into four components (product, price, promotion and place) in accordance with the theory of marketing. One of the components is a product promotion. But now this element is becoming a fully virtual tool. It is necessary to consider product promotion as an image or a copy of a real product in a virtual space that lives in parallel on the network. Therefore, the objective of the paper is the presentation of a new object of research based on the experience of more than thirty real projects performed in Ukraine, USA, Europe and Canada. We regard the promotion as a software product, which works according to principles of knowledge management and machine learning. It is proposed that virtual promotion is characterized by four views: customer or user, data, technology and marketing. Thus, the structure of virtual promotion business process was presented. It includes four steps: selection of hypertext sources, knowledge representation and extraction, semantic kernel building and quality criterion evaluation to stop the process. Based on the process structure the research tasks were identified. The central task is semantic kernel forming. Then the software architecture was developed. IT solution contains CRM system as accounting tool and Web site as an image of virtual promotion. CRM plays main role as a commander center. Here we form semantic kernel and then send it via marketing channels such as Web site, telegram or viber accounts. Another part of IT solution is Web service such as Bing API or Google API. They help us to build the kernel. Also the paper demonstrates the list of future tasks that should be solved and the example of real project of proposed approach.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".