Sociodemographic risk and early environmental factors that contribute to resilience in executive control: A factor mixture model of 3-year-olds
Bibliographic record
Abstract
Young children at sociodemographic risk generally demonstrate lower executive control (EC), although with substantial heterogeneity across children. Given this marked variability, there may be some at-risk children who display higher EC and may be buffered from or resilient to the effects of sociodemographic risk who can be studied to identify the contributory factors. In this study, factor mixture modelling was used to determine whether subgroups of 3-year-old children existed based on their observed performance on a battery of EC tasks. Results indicated 2 latent groups: One characterized by lower EC and the other by higher EC. Both sociodemographically at-risk and low-risk children were represented in each group, yielding 4 risk-status-by-EC groups, where at-risk higher EC children were termed the resilient group. Proximal household enrichment (e.g., exposure to learning materials, varied enriching experiences, academic and language stimulation, parental responsivity) distinguished the resilient group from lower performing children of similar risk status, whereas distal financial resources and proximal social network resources did not distinguish these two groups. Results suggest potential intervention targets to promote optimal EC development, particularly among children at risk.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".