Verbalization of cognitive processes in the texts of bilinguals with alexithymia
Bibliographic record
Abstract
This article describes certain fragments of verbal-lexical and linguistic-cognitive levels of linguistic personality of the bilinguals with alexithymia. The goal lies in their determination, description, and comparison with the corresponding fragments of linguistic personality of the Russians with alexithymia for outlining the parameters that correlate or do not correlate with nationality and alexithymia status of the respondent. The research leans on the linguistic, quantitative and qualitative content analysis of autobiographical texts. The essential condition for including in the number of respondents was a pronounced alexithymia status (diagnosed in accordance with the Toronto Alexithymia Scale), affiliation to Altai or Yakut ethnic group, and command of the corresponding language (bilingualism). The following conclusions were made: certain cognitive and lexical-semantic parameters remain constant, while morphological and punctuation parameters among Altai and Yakut people cease to be the criterion of the pronounced alexithymia status of a person. The acquired results can be used as a complementary instrument for the diagnosis of alexithymia and its correction. The relevance of this research is substantiated by incidence of the phenomenon of alexithymia and the need for conducting comparative study of the texts of persons with alexithymia who belong to different ethnic groups, which allows determining the framework of such supplementary instrument of diagnosis as text analysis.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".