An investigation into the identification of gifted kindergarten children
Bibliographic record
Abstract
Gifted children once identified may develop to their potential if appropriate and stimulating educational programs are provided. There has been a progressive search for valid and economical instruments that can be used for identification of the intellectually gifted. The purpose of this investigation was to determine what combination of three economical predictor instruments was able to identify an intellectually gifted group of kindergarten children. Kindergarten children (n=8l6) from a random sample of 32 schools in two urban school districts of the Greater Vancouver area of British Columbia were administered the Vane Kindergarten Test (VKT). A further sample (n=l94) was drawn from the above sample. The Wechsler Preschool and Primary Scale of Intelligence (WPPSI) was administered to the 194 children. The teachers and parents of these children (n=l94) completed the questionnaires: Perks Teacher Nomination Questionnaire (PTNQ) and Perks Parent Nomination Questionnaire (PPNQ), on children's intellectual abilities. On the basis of their WPPSI scores, the children were classified into gifted (n=42) (WPPSI IQ2.130) and non-gifted (n=l52) (WPPSI IQ<130) groups. A discriminant function analysis was performed to assess the validity of the VKT, PTNQ, and PPNQ as predictors of group membership. Of the 194 children, 88.7% were identified correctly as gifted or non-gifted by the VKT, 79.9% by the PTNQ, and 78.4% by the PPNQ. For the 152 non-gifted children, 95.4% were identified correctly by the VKT, 96.1% by the PTNQ, and 94.7% by the PPNQ. Of the 42 gifted children, 64.3% were identified correctly by the VKT, 21.4% by the PTNQ, and 19.0% by the PPNQ. The VKT was the most accurate predictor (64.3%) of intellectually gifted kindergarten-children and was of particular value (97.6%) if children with VKT IQ scores of 127, 128, and 129 as well as those with scores 130 and higher were included within the gifted group.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".