Language skills differences between adults without formal education and low formal education
Bibliographic record
Abstract
BACKGROUND: The influence of education on cognition has been extensively researched, particularly in countries with high levels of illiteracy. However, the impact of low education in all cognitive functions appears to differ. Regarding to language, the effects of education on many linguistic tasks-supported by different processing-remain unclear. The primary objective of this study was to determine whether oral language task performance differs among individuals with no formal and low-educated subjects, as measured by the Brazilian Montreal-Toulouse Language Assessment Battery (MTL-BR). This is the only language battery available for use in Brazil, but lacks normative data for illiterate individuals. The secondary objective was to gather data for use as clinical parameters in assessing persons with aphasia (PWA) not exposed to a formal education. METHODS: A total of 30 healthy illiterate individuals aged 34-60 years were assessed. All participants underwent the MTL-BR Battery, excluding its written communication tasks. The data obtained in the present study were compared against results of a previous investigation of individuals with 1-4 years of education evaluated using the same MTL-BR instrument. RESULTS: Statistically significant differences in performance were found between non-formal education and the low-educated (2-4 years) groups on the tasks Auditory Comprehension, Repetition, Orthographic/Phonological Fluency, Number dictation, Reading of numbers and also on simple numerical calculations. CONCLUSION: The study results showed that individuals with no formal education/illiterate had worse performance than low-education individuals on some of the language tasks of the MTL-Br Battery, suggesting that each year of education impacts cognitive-language performance. Also, data were obtained which can serve as a guide for PWA not exposed to a formal education.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".