Re-Conceptualizing Developmental Areas of Assessment for Screening, Eligibility Determination and Program Planning in Early Intervention
Bibliographic record
Abstract
Contemporary recommended practices in early childhood assessment strive to gain a holistic picture of child learning and development to inform screening, eligibility, and program planning decisions. These practices have traditionally focused on competencies reflected in developmental domains with limited attention to the approaches-to-learning used to acquire those competencies. In this article, we call for the examination of early childhood constructs that impact a child’s ability to learn and develop, such as executive function (EF), mastery motivation, self-regulation and self-determination, specifically in the infant-toddler period. With EF defined as a wide range of central control processes in the brain that link and categorize information that is discernible in cognitive, motor, and behavioral responses [1], we propose a model of EF as the core construct that drives and unites these learning processes and describe how the model can be applied to Part C early intervention screening, assessment, eligibility determination, and program planning, as well as identify future directions in research and personnel preparation.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| 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".