Solitary groups: A latent profile analysis of motivations for social withdrawal and experiences of solitude in late childhood and early adolescence
Bibliographic record
Abstract
Abstract The present study aims to differentiate groups of children and early adolescents characterized by their motivations for social withdrawal and personal experiences with solitude. Participants were N = 561 (307 girls) children and early adolescents, aged 8–14 years (M = 11.32, SD = 1.63), who completed self‐report assessments of motivations for social withdrawal (i.e., shyness, unsociability), social/asocial dissatisfaction (i.e., loneliness, aloneliness), time alone, affect during solitude, personality traits (i.e., Big Five), and indices of internalizing difficulties (i.e., social anxiety, depression). Results from a Latent Profile Analysis (LPA) provided evidence of three distinct groups characterized by different motivations for social withdrawal and experiences with solitude: (1) the shy group, characterized by higher levels of loneliness, social anxiety, depression, and emotional instability; (2) the unsociable group, who reported higher levels of aloneliness and average scores of extraversion and internalizing difficulties; and (3) the sociable group, characterized by lower levels of both loneliness and aloneliness, and higher levels of extraversion. Overall, findings confirmed the heterogeneity in how children and early adolescents experience solitude, their motivations, and individual dispositions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".