Bibliographic record
Abstract
Introduction. Currently, the scale of cyber addiction, Internet addiction, gadget addiction and other non-chemical addictions associated with interactive technologies is so significant that they are singled out in a number of new addictions that significantly affect the formation of young people and society as a whole.Purpose – the analysis of empirical data on the identification of alexithymia in young men with manifestations of cyber addiction.Research methods. Bibliographic and historical analysis, psychodiagnostic (test method "Toronto Alexithymia Scale" (TAS)), mathematical and statistical.Research results. In order to carry out a detailed verification of the signs of cyber addictions, alexithymic manifestations were studied in young men with manifestations of cyber-addictions.The study involved 269 boys aged 14-21 years. This made it possible to more clearly understand the generation of manifestations of aggression, anxiety, depression, conflict and disturbances in the interpersonal communication of male cyber-addicts. According to the results of the data obtained, it was found that the majority of young men in the age category from 14 to 21 years old do not show manifestations of cyber-addictions – non-alexithymic (more than 50%), and respondents with cyber-addictions have manifestations of alexithymia in 45%-50% of respondents.Conclusions. Thus, it is possible to characterize young men with manifestations of cyber-addictions as people who mainly have manifestations of alexithymia, are incapable of reflection, are prone to the manifestation of short-term, sharply expressed in behavior emotional outbursts, the causes of which they are poorly aware of, and also have manifestations of depression and anxiety.The study made it possible to include alexithymic manifestations in research markers for the further development of psychocorrectional programs for adolescents suffering from various types of cyber-addictions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".