Bibliographic record
Abstract
Standard texts in neuropsychology, forensic, and educational psychology recommend the use of percentile rank scores (PRs) in reports and in oral “feedback” on the grounds that percentiles are easily understood by nonpsychologists. This study tested that assumption, testing predictions that errors would be made consistent with misunderstanding PR values as units of equal intervals. Four hypotheses about errors in interpretation were tested using a 12-item task to assess third-year psychology undergraduates' estimates comparing PR scores against the familiar metric of IQ. All predictions of significant asymmetrical and systematic errors of interpretation were supported. Even psychometrically educated subjects grossly misinterpret the meaning of PRs. Commonly recommended graphical display formats may unwittingly enhance these errors. Implications are most significant for forensic neuropsychology. Apparently low PRs representing cognitive performance within the Average range will typically be misinterpreted to mean significant impairment and, thus, may distort compensation and personal injury awards. Conversely, apparently high PRs representing offender recidivism risk within the Average range will be misinterpreted as high risk and impede release.
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 | 0.023 | 0.241 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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".