Neurocognitive Profiles of Children With High Intellectual Ability: A Pilot Study
Bibliographic record
Abstract
A common question among teachers of students with high intellectual ability is how to best teach this group of children. To answer this question, it is first necessary to better understand their cognitive profiles. Thus, the primary goal of this study was to examine the neurocognitive profiles of children with high intellectual ability. To do this, we used the Discrepancy Consistency Model (Naglieri, 1999), which allows researchers to detect patterns of cognitive strengths and weaknesses. One hundred forty-two children with high intellectual ability (70 females, 72 males; Mage = 127.41 months, SD = 10.76) from Grades 4, 5, and 6 were assessed on measures of general intelligence and academic achievement, as well as on measures of Planning, Attention, Simultaneous, and Successive (PASS) processes. Results showed that 54% of the sample had a PASS score that was significantly lower than that of each student’s average PASS score. Only 8% of the students had a PASS disorder (a score that was low in relation to the student’s average and below 90). Further, 4% of our sample had both a PASS disorder and an academic-skills disorder. The findings suggest that students with high intellectual ability can show variability in PASS scores that may have relevance for instructional programming and for identifying twice-exceptional children.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".