Comparison of Literal, Inferential, and Intentional Text Comprehension in Children with Mild or Severe Closed-Head Injury
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Children with head injury have impairments in pragmatic language at the level of both single words and texts. Text comprehension deficits are likely to be the more consequential for everyday and academic function, yet the relative magnitudes of literal and nonliteral text comprehension deficits have not been measured. DESIGN: We compared the magnitude of the impairment in three forms of text comprehension for children with mild or severe head injury relative with controls: literal language (understanding literal text information), inferential language (making pragmatic inferences, textual coherence inferences, or enriching inferences), and the language of mental states and intentions (eg, producing speech acts, appreciating irony, and understanding deception). MEASURES: Effect sizes were used to measure the magnitude of the difference between children with head injury and age-matched controls. RESULTS: Children with severe closed-head injury were significantly impaired on tasks of literal text understanding, inferencing, and intentionality. Children with mild head injury were impaired on some inferencing and all intentionality tasks, although they had no literal text comprehension deficits. CONCLUSIONS: For both groups, the greatest deficits (ie, the largest effect sizes) were on tasks requiring understanding of the language of mental states and intentions. The data bear on the long-term effects of childhood closed-head injury on text- and discourse-level language and also on the nature and timing of language rehabilitation in children with head injury.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it