Bibliographic record
Abstract
Introduction This chapter outlines approaches to top-down cognitive modelling and inferencing, and addresses functionally grounded work on affect. We describe each area and illustrate its potential for addressing questions in clinical discourse analysis. We also review recent work from neuroimaging and lesion studies to suggest some of the relevant neural systems. As usual, we draw on various disciplinary perspectives and theoretical models. Our practical motivation here is to use what works, and has potential for coding corpora in the various linguistic contexts and situations encountered doing clinical discourse analysis. Cognitive models in general characterize information bundles of various kinds. Perhaps the most familiar are those used to represent words or word-like concepts. Models for words may be more or less detailed depending on the tolerance for elaboration within a particular framework, but morphosyntactic class, inflection and distribution features are typically indicated. How a word is pronounced – its phonological form and regular phonetic variants – will be spelt out in phonological and phonetic representations. Semantic features are often specified only at superordinate levels as in THING/EVENT or merely indexed through the use of the ‘CAPS-for-concept’ convention. Thus, the model for the lexeme ‘cat’ will include the information that it is a common count noun, with the inflectional and distributional features of this class – it can occur as head of a noun phrase and it inflects for plural number /s/. It is pronounced /kæt/.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".