The Cognitive and Mathematical Profiles of Children in Early Elementary School
Bibliographic record
Abstract
The present study investigated the diverse cognitive profiles\nof children learning mathematics in early elementary school.\nUnlike other types of learning difficulties, mathematics\nimpairments are not characterized by a single underlying\ncognitive deficit, instead multiple general and numeracyspecific\ncognitive skills have been proposed to underlie\nmathematics ability. Combining theory- and data-driven\napproaches, the study investigated cognitive mathematics\nprofiles. Participants for this study were 97 children tracked\nfrom senior kindergarten to grade two, as part of the Count\nMe In Study. Using numeracy, working memory, receptive\nlanguage, and phonological awareness factors, a two-step\ncluster analysis revealed a three-cluster solution. The groups\nwere characterized as (1) above average overall, (2) average\noverall with weak visuospatial working memory, (3) poor\noverall with strong visuospatial working memory. Cluster 1\ndemonstrated strengths in mathematics and reading,\ncompared to clusters 2 and 3. Developmental trends and\npotential interventions 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".