Humanitarian “Reversing” in the Education Development in the Russian Federation Education under the Conditions of Transhumanistic Challenges in the Era of Globalisation
Bibliographic record
Abstract
The effectiveness of the development of individual countries within the conditions of accelerating scientific and technological progress, expanding the influence of globalisation and integration processes in the global socio-cultural space are very important during the exchange of international students. The purpose of the paper is to research the methodological foundations of the humanities education for international students as an alternative to transhumanist ideology. In the framework of general scientific research methods, a paradigmatic approach is used as a factor analysis of students' educational processes. Particular attention is paid to the substantiation of the methodological foundations of the education humanisation on the basis of modern ontologically anthropological and religious-anthropological achievements of the philosophical field of knowledge. In this regard, the thesis is accepted that the artificially imposed transhumanist ideology performs a key integrating function when considering the practice of education of international students. The novelty of the study is due to a discussion of the contradictions caused by all-round technocratisation and informatisation, the fall of moral standards, and the degradation of human behavior. The practical significance of the study is determined by the need to develop a different pedagogy and model of education for modern students.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".