Effects of age and school type on unconstrained, phonemic, and semantic verbal fluency in children
Bibliographic record
Abstract
Biological and cultural factors have been found to have a significant influence on cognitive development and performance in neuropsychological instruments such as verbal fluency tasks (VFT). Variations of traditional VFT, involving unconstrained word production and increased retrieval times, may provide further data regarding the executive, attentional, mnemonic, and linguistic abilities involved in VFT. As such, the aim of this study was to investigate the impact of age and school type on the performance of 6- to 12-year-old children in unconstrained, phonemic, and semantic VFT. The VFT were administered to 460 participants. The effects of age and school type on verbal fluency (VF) performance were analyzed using a two-way analysis of variance, followed by Bonferroni post-hoc tests (p ≤ .05). A repeated-measures analysis was also used to evaluate VF performance over time (p ≤ .05). Main effects of age and school type were identified on all measures (effect sizes ranged from .05 to .32, p ≤ .05). VF scores increased with age and were higher among private school students. The influence of age on VFT may be associated with the development of executive functions. The impact of type of school on VF performance may be explained by the greater availability of cognitive stimulation (semantic knowledge) provided by private schools and families with better socioeconomic levels.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".