Schizophrenia: The Quest for a Minimum Sense of Identity to Ward off Delusional Disorder
Bibliographic record
Abstract
OBJECTIVE: This study was designed to analyze the language of patients with schizophrenia exhibiting negative symptoms during a 3-month period. METHOD: The computer-assisted ALCESTE method was used to simultaneously analyze the subjects' oral behaviour and speech patterns at various levels. RESULTS: The tested subjects had very specific speech patterns. Most significantly, analysis of the underlying syntactic processes showed that the patients exhibited a sense of identity, however minimum, based on their own pathologies and on the surrounding world. In our previous study, no such characteristics were observed in the discourse of schizophrenia patients with delusions (exhibiting positive symptoms). This suggests that the minimum sense of identity that develops in patients with schizophrenia allows them to avoid positive symptoms. CONCLUSION: In studies of language production by subjects suffering from schizophrenia, it is necessary to distinguish between patients with positive symptoms and those with negative symptoms. The speech patterns of these 2 groups have to be analyzed separately, which has not been done previously, since the groups differ in too many respects.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".