Bibliographic record
Abstract
The title Bender Gestalt: Screening for Brain Dysfunction (2nd ed.) indicates that the primary utility of the Bender Gestalt Test (BGT) is one of screening for the presence of brain impairment. The author, Patricia Lacks, quickly dispels this notion in the preface to her book where she states, “My book is not about how to use the BGT as a single test of ‘organicity’, a long outdated practice. Instead, the focus is on neuropsychological assessment as a continuum” (p. vii). Indeed, Lacks advocates, throughout her book, the more general use of the BGT as an important part of any standard neuropsychological test battery. She writes, “Even though the BGT has been shown to be useful for identifying persons with a wide range of cognitive impairment, it primarily assesses disordered perceptual-motor and executive functions” (p. 27). Unfortunately, Lacks does not provide the reader with any data to support her above statement regarding what the BGT actually measures. Before taking the latter point any further, allow me to briefly describe the BGT and its history.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".