Emotion recognition, self-knowledge, and perceptions of leisure time activities in emerging adolescents: A longitudinal study
Bibliographic record
Abstract
This 2-year short-term longitudinal study explored Canadian emerging adolescents’ ability to recognize emotions in others, their spontaneous descriptions of themselves and self-understandings, and their narrative and pictorial accounts of themselves engaged in leisure time activities. As part of a larger 5-year longitudinal study, this study describes results from Time 1 (2015–16), 146 Grade 8 students, 97 female, M age = 12.5 y, and Time 2 (2016–17) data from 46 Grade 9 students (33 females; M age = 13.5 y) from eight schools . Participants completed the Reading the Mind in the Eyes Test, self-descriptions, drew and/or wrote a story to describe an enjoyable leisure time activity, and a self-understanding interview (SU). Self-descriptions and stories were also coded for the frequency of mental state language (MSL). Results showed positive correlations between T1 Emotion Recognition (ER) and T1 MSL in drawings of leisure time and between T2 ER and T2 SU. Higher levels of ER in younger adolescents predicted higher levels of SU 1 year later, and T1 SU predicted T2 ER. Across 2 years, ER remained stable, older adolescents scored higher than younger on SU, and girls scored higher than boys on ER. Implications for theory and educational applications are discussed.
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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 | 0.003 |
| 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 | 0.001 | 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".