Bibliographic record
Abstract
A survey addressing practices of 'expert' neuropsychologists in handling financial compensation claim or personal injury litigation cases was carried out. Potential participants were identified by publication history. Responses were obtained from 24 out of the 39 neuropsychologists who were surveyed. Approximately 79% of the respondents reported using at least one specialized technique for detecting malingering in every litigant assessment. Half stated that they always give specialized tests at the beginning of the assessment. The Rey 15-Item test and the Test of Memory Malingering were the most frequently reported measures. Respondents also reported frequent use of 'malingering' indexes from standard neuropsychological tests. Reported base-rates varied, but the majority of respondents indicated that at least 10% of the litigants they assessed in the last year were definitely malingering. Respondents were split on the practice of routinely giving warnings at the outset of assessments that suboptimal performance may be detected. However, when the client's motivational status was suspect, more than half (58.3%) altered their assessment routine at least on some occasions, by encouraging good effort (70.8%) or administering additional SVTs. A minority directly confronted or warned clients (25%), terminated the examination earlier than planned (16.6%), or contacted the referring attorney immediately (29.2%). Respondents almost always stated some opinion regarding indicators of invalidity in written reports (95%). However, 41.7% rarely used the term 'malingering' and 12.5% never used the term. Most respondents (>80%) instead stated that the test results are invalid, inconsistent with the severity of the injury or indicative of exaggeration.
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 | 0.003 | 0.016 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".