Bibliographic record
Abstract
This study evaluated the concurrent validity of estimated Wechsler Adult Intelligence Scales–Third Edition (WAIS-III) index scores using various one- and two-subtest combinations. Participants were the Canadian WAIS-III standardization sample. Using all possible one- and two-subtest combinations, an estimated Verbal Compre-hension Index (VCI), an estimated Perceptual Organization Index (POI), and an esti-mated Working Memory Index (WMI) were generated by prorating relevant subtest scores. As expected, two-subtest short forms were consistently more accurate than one-subtest short forms. Agreement between short-form and full-form index scores was high for two-subtest combinations (range = 88 % to 96%) but only moderate with one subtest (range = 62 % to 79%). Accuracy did not vary by age, ethnicity, gender, or education. However, accuracy was lowest for index scores in the high average to very superior range. These results suggest that although some two-subtest short forms are useful for estimating VCI, POI, and WMI scores, one-subtest short forms should not be used for this purpose.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.196 | 0.051 |
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".