Bibliographic record
Abstract
The Boston Naming Test (BNT) (Kaplan, Goodglass, & Weintraub, 1983 Kaplan, E., Goodglass, H., & Weintraub, S. (1983). The Boston Naming Test. Philadelphia, PA: Lea & Fibiger. [Google Scholar]) is the most commonly used test of confrontation naming in neuropsychology (Rabin, Barr, & Burton, 2005 Rabin, L., Barr, W., & Burton, L. (2005). Assessment practices of clinical neuropsychologists in the United States and Canada: A survey of INS, NAN, and APA Division 40 members. Archives of Clinical Neuropsychology, 20, 33–65. doi:10.1016/j.acn.2004.02.005.[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]). However, there are significant criticisms of the BNT which suggest that it might not be the assessment measure of choice. These criticisms are that the BNT has poor psychometric properties, is not adequately standardized, and has inadequate norms. It is further suggested that when considered in the context of contemporary conceptualizations of the neuropsychology of naming, the BNT does not adequately capture the processes known to be required for successful naming, and does not sample widely enough from the content domain of “naming”. These criticisms suggest that the BNT is flawed as a measure of naming, and are discussed in detail in this review. Other stand-alone visual confrontation naming tasks are reviewed to evaluate whether any might be viable substitutes for the BNT in clinical neuropsychology. The Naming Test from the Neuropsychological Assessment Battery (Stern & White, 2009 Stern, R., & White, T. (2009). NAB Naming Test: Professional manual. Lutz, FL: Psychological Assessment Resources. [Google Scholar]) was identified as a possible alternative to the BNT, however, neither of these tests was designed with reference to models of the neuropsychology of naming, and development of a new test of naming is indicated.
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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 | 0.025 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".