Interpreting the <scp>WISC–V</scp> from a Neuropsychological Perspective
Bibliographic record
Abstract
This chapter provides a general overview of neuropsychologically oriented approaches, focusing on Lurian- and process-oriented approaches that stem from the work of Edith Kaplan and her colleagues. Specialization in clinical neuropsychology is recognized by the American Psychological Association (APA) and the Canadian Psychological Association. The practice of clinical neuropsychology “can be depicted as having a long history and a short past”. According to the APA website, specialized training for clinical neuropsychology includes knowledge of neuroanatomy, neuroscience, brain development, neurological disorders, neurodiagnostic techniques, and normal and abnormal brain function. Silver's information-processing model, for example, has historically been a foundation of Kaufman's intelligent testing approach. Although there is much variety in neuropsychological report writing, there are also commonalities. The illustrative case reports selected for this chapter all provide examples of neuropsychologically oriented approaches to interpretation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".