Bibliographic record
Abstract
This book is predicated on the relatively uncontentious notions that discourse patterns – what people do when they talk or write – can provide trained observers with information about cognitive functions and affective states in speakers and, further, that cognitive functions and affective states may be signs of integrity of neurological function and structure. Neurolinguists, psycholinguists, aphasiologists, psychiatrists, psychotherapists and speech pathologists all take some variation on assumptions like this as their point of departure in studying brain–behaviour relationships and treating some neurological and affective disorders. However, discourse – people's talk and text – is inherently complex and apparently unstable and, worse, the neurological substrate and processes that support even superficially simple things like ‘how words are represented in the brain’, let alone ‘what happens in brains when people talk’ are matters of active debate and investigation rather than scientific givens. In the face of so much uncertainty and complexity, most of the work done on language–brain relationships has, very sensibly, centred on theoretically discrete and/or methodologically isolatable phenomena associated with particular semantic, morphosyntactic or phonological structures or processes. Work on discourse in clinical environments as another means of investigating neurocognitive (dys-)function, although often called for, has been less common. This situation is changing now because of technological developments and, we think, a sea-change-like shift that is taking place in attitudes to brain–behaviour relationships.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.503 | 0.344 |
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".