Who gets caught at maturity gap? A study of pseudomature, immature, and mature adolescents
Bibliographic record
Abstract
This research examined links among adolescents’ maturity status, their biological, social, and psychological characteristics, and parents’ perceptions of their adolescents’ maturity. The participants were 430 Canadian adolescents in the sixth and ninth grades, and a subsample of their parents. Pattern-centred analyses confirmed the existence of three clusters of adolescents differing in maturity status: pseudomature (25%), immature (30%), and mature (44%). Further analyses found differences among the clusters in adolescents’ pubertal status, the social context (presence of older siblings and friends), and their desired age, involvement in pop culture, school and peer involvement, and close friendships. Analysis of mother and father reports revealed some differences in how parents of pseudomature, immature, and mature adolescents perceived their adolescents’ maturity, and in how they felt about their adolescents’ maturity. There were few grade differences in the findings. The results suggest that pseudomature adolescents, and to a smaller extent, immature adolescents, are caught in a maturity gap, which could have longer-term implications for their transition to adulthood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".