Latent Semantic Structure of the WMS-III Verbal Paired-Associates
Bibliographic record
Abstract
OBJECTIVE: To investigate the factor structure of the verbal paired-associates (VPA) subtest in the WMS-III using a theoretically driven model of semantic processing previously found to be well-fitting for the WMS-IV version of the test. METHOD: Archival data were used from 267 heterogeneous neurosciences patients and 223 seizure disorder patients who completed the WMS-III as part of a standard neuropsychological evaluation. Confirmatory factor analysis was used to test theoretically driven models for VPA based on principles of semantic processing. Four nested models of different complexities were examined and compared for goodness-of-fit using chi-squared difference testing. Measurement invariance testing was conducted across heterogeneous neuroscience and seizure disorder samples to test generality of the factor model. RESULTS: After removing items with limited variability (very easy or very hard; 12 of 40 items), a four-factor model was found to be best-fitting in the present patient samples. The four factors were "recreational", "functional", "material", and "symbolic", each representing semantic knowledge associated with the function of the target word referent. This model subsequently met the criteria for the strict measurement invariance, showing good overall fit when factor loadings, thresholds, and residuals were held to equality across samples. CONCLUSIONS: The results of this study provide further evidence that "arbitrary" associations between word pairs in VPA items have an underlying semantic structure, challenging the idea that unrelated hard-pairs are semantic-free. These results suggest that a semantic-structure model may be implemented as an alternative scoring in future editions of the WMS to facilitate interpretation.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".