STRUCTURAL DEFICIENCY IN SCHIZOPHRENIA: AN EXPLORATORY STUDY OF THE NOMINAL AND SENTENTIAL DOMAINS
Bibliographic record
Abstract
The present dissertation is an exploratory study of structural deficiency in schizophrenia, in which the usage of subject pronouns and type of sentence in narratives produced by native speakers of Colloquial Brazilian Portuguese diagnosed with schizophrenia is investigated.Couched within Generative Grammar Theory, in which human grammar is defined as a computational cognitive device, we explored the hypothesis that schizophrenia leads to structural impoverishment at the syntactic level.Two corpora of narratives of dream and waking reports were examined considering subject pronouns and type of sentences.Overall, our results showed significant higher proportion of matrix sentences and null pronouns, particularly of 3Person referential null pronouns in the schizophrenia group as compared to the control group.These findings are in line with the hypothesis of structural impoverishment in schizophrenia, especially if null pronouns are taken to be elements with reduced structure in comparison to full pronouns.Also, our results corroborate with the hypothesis that grammar in the face of schizophrenia might present a deficit in terms of functional categories, which leads to structural impoverishment and to anomalies in the referential use of pronouns (Tovar et al., 2019).Our findings are thus extra evidence that structural deficiency is a universal feature of schizophrenia, while suggesting manifestations of this deficiency is language dependent, being, thus, subject to parametric variation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".