MétaCan
Menu
Back to cohort
Record W2883827695 · doi:10.1037/ort0000351

Inclusive early childhood development (IECD): A twin-tracking approach to advancing behavioral health and social justice.

2018· article· en· W2883827695 on OpenAlexaff
Donald Wertlieb

Bibliographic record

VenueAmerican Journal of Orthopsychiatry · 2018
Typearticle
Languageen
FieldHealth Professions
TopicChild and Adolescent Health
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsPsycINFOConceptualizationEarly childhoodPsychologySustainable developmentEconomic JusticeMainstreamingPolitical scienceDevelopmental psychologyMEDLINESpecial educationLaw

Abstract

fetched live from OpenAlex

As our American Orthopsychiatric Association (AOA) transforms into a Global Alliance for Behavioral Health and Social Justice (GABHSJ), early childhood development (ECD), and, particularly, inclusive early childhood development (IECD) persists as a prime pathway toward enhancing behavioral health and social justice. As we systematically and intentionally include consideration of the rights and needs of young children with disabilities and their families in our research, practice, and policy, a twin-track (TT) approach that simultaneously considers universal factors alongside disability-specific factors can enhance our conceptualization of problems and solutions. In the context of the sustainable development goals (SDGs) of the UN 2030 Agenda for Sustainable Development and its commitment to "leave no one behind," behavioral health and social justice enhancements derive from "mainstreaming" disability matters into diverse universal policies and programs. Elaborated in the triple-twin-track approach is a call for balancing child-centeredness, family focus, and community concerns as well as integrating the special and deepening knowledge of infants and young children with extant social policy and practice. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.550
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.023
GPT teacher head0.386
Teacher spread0.363 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of OrthopsychiatrySame topicChild and Adolescent HealthFrench-language works237,207