Positive Illusory Bias and the Self-Protective Hypothesis in Children With Learning Disabilities
Bibliographic record
Abstract
We tested the hypothesis that overestimations of performance by children with learning disabilities (LD) are self-protective and will dissipate following positive feedback. Twenty-three boys and 17 girls with LD (ages 10.6 to 13.5 years) and a control group of non-LD matched children (22 boys and 17 girls) provided a prediction of their performance on a spelling test prior to completing the test. Subsequently, they were randomly assigned to either a positive feedback or a no-feedback condition. Finally, they provided a second prediction of performance on an equivalent spelling test. In children with LD, there was a positive bias in their predictions of performance, and, following positive feedback, their predictions became accurate. In children without LD, there was no positive bias and no effect of feedback. The results provide further support for the presence of a positive illusory bias and for the self-protective hypothesis in children with LD.
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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 | 0.002 | 0.022 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".