The emotion word fluency test as an embedded performance validity indicator – Alone and in a multivariate validity composite
Bibliographic record
Abstract
OBJECTIVE: This project was designed to cross-validate existing performance validity cutoffs embedded within measures of verbal fluency (FAS and animals) and develop new ones for the Emotion Word Fluency Test (EWFT), a novel measure of category fluency. METHOD: The classification accuracy of the verbal fluency tests was examined in two samples (70 cognitively healthy university students and 52 clinical patients) against psychometrically defined criterion measures. RESULTS: -score of ≤31 on the FAS was specific (.88-.97) to noncredible responding in both samples. Animals T ≤ 29 achieved high specificity (.90-.93) among students at .27-.38 sensitivity. A more conservative cutoff (T ≤ 27) was needed in the patient sample for a similar combination of sensitivity (.24-.45) and specificity (.87-.93). An EWFT raw score ≤5 was highly specific (.94-.97) but insensitive (.10-.18) to invalid performance. Failing multiple cutoffs improved specificity (.90-1.00) at variable sensitivity (.19-.45). CONCLUSIONS: Results help resolve the inconsistency in previous reports, and confirm the overall utility of existing verbal fluency tests as embedded validity indicators. Multivariate models of performance validity assessment are superior to single indicators. The clinical utility and limitations of the EWFT as a novel measure are discussed.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".