Examining the Relationship Between the Overexcitabilities and Self-Concepts of Gifted Adolescents via Multivariate Cluster Analysis
Bibliographic record
Abstract
The purpose of this study was to explore the relationship between gifted adolescents’ forms of overexcitabilities and self-concepts. Clusters of adolescents were formed on the basis of their overexcitabilities, and these clusters of adolescents were then compared with regard to their self-concept scores. Gender differences were also examined. The sample consisted of 379 gifted adolescents, ranging in age from 11 to 16 years of age. Forms of overexcitabilities were measured using the Overexcitabilities Questionnaire—II, and various facets of self-concept were measured using the Self-Description Questionnaire—II. Using cluster analysis, multivariate analysis of variance, and chi-square analysis, results suggested a distinct four-cluster solution, as well as differences between clusters in self-concept and gender. Putting the Research to Use Within this research, four distinct clusters of adolescents were found, namely a Low Imaginational group, a High Intellectual group, a Low Imaginational/High Psychomotor group, and a Low Psychomotor group. Differences in self-concept were found to center on the Low Psychomotor group, such that this group scored significantly lower than the three other groups with regard to various facets of self-concept. Females significantly outnumbered males in the Low Psychomotor group. Thus, gifted adolescent females with a low psychomotor overexcitability score may be more prone to a lowered self-concept and may need intervention, counseling, or special activities/accommodations to buffer the potential self-concept deficits they may face.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".