Digital environment as effective pedagogical tool of socialization in the context of modern education
Bibliographic record
Abstract
Today, the modern educational paradigm is based on the organization of new forms of social education and training with new digital technologies introduction. In the article, the author sproceed from the main conceptual provisions of domestic and foreign the ories, which allow us as serting that the pedagogically justified use of the digital environment, aimed at solving the problems of social education, can contribute to the adaptation of students to life in the modern in formation society. The purpose of the study is to identify the problems and perspectives of the students’positive socialization formation under the conditions of modern education based on the theoretic alanalys is of domestic and English-speaking (the USA and Canada) scientific works that reflect the content of the digital technologies implementation. This article at tempts to: reveal the essence of the digital environment concept as a pedagogical tool for students’ positive socialization, where the digital environment represents an element of the information and educational environment, with in which socialization is revealed, where the cybers pacesocial process espotential comprehension is carried out; determine the social and educational effects, the main conceptual provisions of the digital environment use as the pedagogical tool for the students’ positives ocialization; based on the analysis, expand knowledge in the context of the theoretical and methodological aspects for domestic sciencein view of the research of scientific works in modern English-speaking countries (the USA, Canada).
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".