Bibliographic record
Abstract
Language is complicated and so, therefore, is language assessment. One complication is that there are many reasons to undertake language assessments, each of which may have different methods and goals. In this article I focus on the specific difficulties faced in aphasia assessment, the assessment of acquired language deficits. As might be expected, the history of aphasia assessment closely mirrors the history of our understanding of the neurological underpinnings of language. Early assessment was based on classical disconnection theories, dating from the 19th century, that conceptualized language as consisting of independent connected modality-specific language centers that could be disconnected by brain damage. Although these models were recognized early on as being too simplistic, aphasia assessment instruments followed the models until quite recently due to the lack of any fully specified alternative language model. It was only in the 1990s, after aphasiology had come increasingly under the influence of experimental psycholinguistics, that attempts were made to create aphasia assessment instruments that did not explicitly follow disconnection models. The most successful of these is the Psycholinguistic Assessment of Language Processing in Aphasia (PALPA; Kay, Coltheart, & Lesser, 1992). These psycholinguistically influenced instruments conceptualize language as a complex multi-dimensional system consisting of many partially independent sub-systems that may be compromised to a greater or lesser degree. Aphasia assessment instruments become longer and more detailed as a reflection of our growing understanding of the complexity of the language system. As they do, the problem of collating and integrating assessment information becomes more intractable. The future of aphasia assessment will require increasing automation to deal with the large amounts of information that must now be synthesized to fully characterize an individual deficit. I discuss recent attempts to computerize aphasia assessment and what benefits they can offer over traditional pencil-and-paper instruments.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".