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Record W4309709624 · doi:10.1097/iyc.0000000000000232

A Conceptual Model for a Blended Intervention Approach to Support Early Language and Social-Emotional Development in Toddler Classrooms

2022· article· en· W4309709624 on OpenAlex
Jennifer E. Cunningham, Jason C. Chow, Kathleen Artman-Meeker, Abby L. Taylor, Mary Louise Hemmeter, Ann P. Kaiser

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInfants & Young Children · 2022
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsGolder Associates (Canada)
Fundersnot available
KeywordsPsychological interventionToddlerIntervention (counseling)PsychologyLanguage developmentClass (philosophy)Conceptual modelSocial emotional learningComputer scienceDevelopmental psychologyArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this article is to present a theory-driven blended intervention model that integrates evidence-based interventions to support language and social development of young children. We (1) provide an overview of practices that are designed to support language and social-emotional development, (2) present a theory of change model that outlines the theoretical basis for our proposed approach, and (3) provide an example of the conceptual model via the blending of Tier 1 interventions that provide class-wide language and behavioral support for young children. We conclude by arguing for the parsimony that a proactive synergy between social and language interventions blended into a single professional development approach will provide.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.713
Threshold uncertainty score0.743

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.022
GPT teacher head0.293
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it