Number Sense in Siblings of Children with Mathematical Learning Disabilities: A Longitudinal Study
Bibliographic record
Abstract
Number sense, counting and logical thinking were assessed in 14 siblings of children with Mathematical Learning Disabilities (MLD) and in 41 age matched children without family members with MLD. The children were tested in kindergarten and followed up in grade 1. A 0-100 number line estimation paradigm with three formats (Arabic digits, dots and number) was used as a measure of number sense. Results reveal that siblings of children with MLD are less proficient in number line placements compared to non-siblings, with both groups having a logarithmic representation in kindergarten and grade 1. Siblings also differ from non siblings on procedural and conceptual counting knowledge and logical thinking in kindergarten. In addition, our findings suggest that nnumber line estimation in kindergarten is especially predictive for untimed procedural calculation performances in grade 1, whereas procedural counting knowledge is related to timed fact retrieval skills in grade 1. Our findings also reveal that MLD had a familial aggregation. Clinical siblings especially differ from non-clinical siblings on the estimation with Arabic numbers (in kindergarten and grade 1) and number words (in grade 1), pointing to the fact that especially symbolic number line estimation tasks on a 0-100 scale can be used as screeners for MLD. Implications for the understanding and diagnosis of MLD are discussed.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".