Formation of Communication of Educational Institutions Using Social Networks
Bibliographic record
Abstract
Objective: The article is devoted to studying the theory and development of social networks' practical aspects in the educational institutions' communication system. The relevance of the research is formed by the growing interest in social networks among young people and the problem of ineffective, outdated teaching technologies that have little involvement among students. The study aims to develop recommendations for social network use in a comprehensive educational institution's communication system. Methods: To solve the question, SWOT analysis, grouping, data analysis, synthesis, and generally scientific methods of induction and deduction are used. Results: The study's results are 1) identification of the student's predisposition to gain knowledge in social networks; 2) identification of effective ways of presentation and educational information; 3) identification of strengths, weaknesses, opportunities, and threats of social networks in the educational process; 4) assessment of the technological possibility of implementing social networks in the educational process; 5) assessment of the possibility of applying different information types for effective communication; 6) a list of recommendations for the implementation of social networks in the overall educational communications system. Conclusions: The novelty of the study is formed by a set of relevant recommendations. The practical significance lies in the implementation of the recommendations in the different countries educational systems.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".