Social Intelligence as a Factor of Socio-Psychological Adaptation of University Students with Special Educational Needs during Distance Learning due to the COVID-19
Bibliographic record
Abstract
The article is devoted to the research of social intelligence as a factor of socio-psychological adaptation of university students with special educational needs during distance learning. Study methods of social intelligence and socio-psychological adaptation are used. The respondents’ contingent of consisted of 78 students with special educational needs. The average level of social intelligence was empirically established in part students, which testifies to their ability to understand properly and the ability to behavior in almost half of life situations. Almost a quarter of students have a higher than average level of social intelligence and the same part is lower than average, which confirms their ability to understand and predict other people’s behavior in almost three quarters and one quarter of life situations, respectively. Students do not have high and low levels of social intelligence. Most students (almost two-thirds) have an average level of adaptability and mal-adaptability; instead, a minority (almost one third) is at a high level. Adaptability and mal-adaptability, respectively, indicate consistency and inconsistency between the needs of the individual and the requirements of the social environment. Students with a low level of adaptability and mal-adaptability were not found. The defined statistically significant relationship of social intelligence with adaptability (direct) and maladaptive (reverse) shows the influence of social intelligence on the effectiveness of socio-psychological adaptation of students. The results of the research proved that social intelligence is a factor of socio-psychological adaptation of students with special educational needs during distance learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".