The personality emotional sphere features on the example of Russian youth in the first quarter of the 21st century
Bibliographic record
Abstract
The article provides a brief theoretical review of the personality emotional sphere, including such phenomena as alexithymia, empathy and emotional intelligence. At the same time, it is noteworthy that the importance of emotions in different spheres of human life is recognized in different branches of psychology and is of key importance both in personal (family, friendly relations, etc.) and in business (business, organization management, teaching, etc.) life of a modern person. Therefore, the authors of the article consider it fundamentally important to develop the emotional sphere of personality, in particular, among young people, because Emotions can help with self-organization and self-motivation in the learning process; however, difficulties in understanding emotions (alexithymia) can negatively affect mental health and indicate the possible presence of psychological trauma in the past. This article also presents the results of an empirical study, namely, conclusions in accordance with the data of correlation analysis. According to the results obtained, it can be argued that the presence of alexithymia is not always associated with underdeveloped emotional intelligence: a person may retain the ability to empathize, probably even with a weak differentiation of ideas about human emotional states. Thus, the hypothesis of a (positive) relationship between high rates of alexithymia and low levels of empathy was not confirmed by the example of a student sample (at the Moscow University of the Humanities and Bauman Moscow State Technical University (Bauman MSTU)).
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".