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Record W2106116343 · doi:10.1177/2158244014529775

Measuring Developmental Differences With an Age-of-Attainment Method

2014· article· en· W2106116343 on OpenAlexaff
Warren O. Eaton, Jennifer L. Bodnarchuk, Nancy A. McKeen

Bibliographic record

VenueSAGE Open · 2014
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMilestoneDevelopmental MilestonePsychologyDevelopmental psychologyChecklistDemographyLongitudinal studyEducational attainmentStatisticsGeographyCognitive psychology

Abstract

fetched live from OpenAlex

The sensitive measurement of variation in rate of attainment is an underutilized but useful indicator of individual differences in development. To assess such individuality, we used longitudinal parental diary checklists of infant attainments to estimate the ages at which ubiquitous developmental milestones like sitting and walking were reached. Parents using this diary checklist have been shown to be valid reporters of milestone attainments. Present analyses show that multiple definitions of milestone onset have high reliability as well. Babies differ considerably in their rates of development, and such individual differences in rates may be predicted from other variables with survival (event history) analysis. Ages of attainment for sustained sitting, crawling, and walking were calculated for 519 infants and predicted using 11 common covariates. Our discovery that babies of younger mothers reach these milestones sooner than those of older mothers reveals the value of an age-of-attainment (AOA) approach. A framework with a SAS program for collecting and analyzing AOA data is presented.

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)0.007
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.124
GPT teacher head0.357
Teacher spread0.233 · 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 designObservational
Domainnot available
GenreMethods

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

Citations5
Published2014
Admission routes1
Has abstractyes

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