LANGUAGE ASSESSMENT OF PRESCHOOL AND SCHOOL-AGE CHILDREN AT RISK FOR LANGUAGE DISORDERS IN RESIDENTIAL CARE SETTINGS
Bibliographic record
Abstract
Adequate language development is central to a child's academic and social development. This study aimed to assess the language of 35 preschool and school-aged Portuguese children in residential care in four social institutions, using the Grelha de Observação da Linguagem (GOL-E) developed by Kay and Santos (2014), a validated tool in Portuguese. The results of the study showed that in terms of language competence, compared to the normative results expected for their age: a) Of the thirty-five children assessed, only three were at or above the 50th percentile; b) Twelve children were between the 5th and 25th percentiles; c) Eight children were in the 10th percentile; d) Of the children between the 11th and 12th percentiles, only one was in the 90th and 75th percentiles; e) Eleven children were in the 10th and 25th percentiles; f) One child at the age of 12 was in the 5th percentile. Most of the children were in percentiles below those expected for their age group. According to the definition of speech and language disorders, we can observe that a group of these children fall under the condition of speech and language disorders, but have not been formally identified in the educational system, putting them at risk of failure in school and life. This study highlights the importance of language assessment and special education services for children living in institutions in Portugal. More studies with this population in these age groups are needed to better understand the language competencies of children living in residential care. Article visualizations:
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".