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Developmental Perspectives on the Study of Persons with Intellectual Disability

2021· article· en· W3128673042 on OpenAlexaff
Jacob A. Burack, David W. Evans, Natalie Russo, Jenilee‐Sarah Napoleon, Karen J. Goldman, Grace Iarocci

Bibliographic record

VenueAnnual Review of Clinical Psychology · 2021
Typearticle
Languageen
FieldNeuroscience
TopicWilliams Syndrome Research
Canadian institutionsSimon Fraser UniversityMcGill University
Fundersnot available
KeywordsIntellectual disabilityPsychologyContext (archaeology)Developmental psychologyPopulationCognitive psychologyMedicinePsychiatryBiology

Abstract

fetched live from OpenAlex

Developmental approaches provide inclusive, universal, and methodologically rigorous frameworks for studying persons with intellectual disability (ID). This is an exceptionally heterogeneous group with regard to etiology, genotype, and phenotype that simply shares the traditional diagnostic criteria, typically a score of two standard deviations below the population mean of 100 on standardized IQ tests and deficits in adaptive behavior. We trace the foundational, conceptual, and methodological roots of developmental approaches and highlight ways that these and more recent iterations continue to be central to advances in the increasingly nuanced study of persons with ID. This work is premised on the consideration of specific etiological groupings and subgroupings across and between different domains of functioning within the context of familial and complex environments throughout the life span. We highlight the potential contributions of advances in behavioral methodologies, genomics, and neuroscience when considered within universal and hierarchic frameworks based on development.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0020.041
Scholarly communication0.0050.005
Open science0.0020.006
Research integrity0.0030.007
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 head0.269
GPT teacher head0.530
Teacher spread0.261 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations44
Published2021
Admission routes1
Has abstractyes

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