Bibliographic record
Abstract
Much of the diagnosis and definition of psychiatric disorders depends on language behaviour. Both the lay and clinical communities recognize that some atypical language behaviour helps to the identify psychiatric disorders. A functional approach to language provides the means to classify the atypicalities in meaning and in wordings that are associated with psychiatric disorders. Culture levels, register, genre, context, vocabulary-grammar, and sound must all be taken into account in considering language atypicalities. Language simultaneously conveys 3 kinds of meaning—ideational, interpersonal and textual. Individually or combined, these 3 kinds of meaning can be used to describe the atypical language behaviour associated with psychiatric disorders. Une large part du diagnostic et de la définition des troubles psychiatriques repose sur le comportement linguistique. Les milieux tant profanes que cliniques reconnaissent qu'un certain comportement linguistique anormal aide à reconnaître les troubles psychiatriques. Une approche fonctionnelle de la langue fournit les moyens de classer les anormalités selon le sens et le choix des termes qui sont associés aux troubles psychiatriques. Les niveaux de culture, le registre, le genre, le contexte, le vocabulaire et la grammaire ainsi que le son doivent tous être pris en compte lorsqu'on examine les anormalités de la langue. La langue transmet simultanément 3 types de sens—idéationnel, interpersonnel et textuel. Individuellement ou combinés, ces 3 types de sens peuvent servir à décrire le comportement linguistique anormal associé aux troubles psychiatriques.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".