Seeing the forest for the trees: Prevalence of low scores on the Wechsler Intelligence Scale for Children, fourth edition (WISC-IV).
Bibliographic record
Abstract
Low scores across a battery of tests are common in healthy people and vary by demographic characteristics. The purpose of the present article was to present the base rates of low scores for the Wechsler Intelligence Scale for Children, fourth edition (WISC-IV; D. Wechsler, 2003). Participants included 2,200 children and adolescents between 6 and 16 years of age from the WISC-IV U.S. standardization sample. Measures considered in the base rates analyses included the 10 core subtests and the 4 index scores. Analyses were conducted for the entire standardization sample as well as stratified by different classifications of intelligence and different years of parental education. In the total sample, it is uncommon to have 6 or more subtest scores or 2 or more Index scores <or= 9th percentile. The prevalence of low scores typically increased with lesser intelligence and fewer years of parental education (e.g., children with below-average intelligence were 75 times more likely than children with above-intelligence to have at least one impaired subtest score). Consistent with existing studies of the base rates of low scores, some low scores on the WISC-IV were common in children and adolescents, and the frequency was related to a child's level of intelligence and parental education.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".