MétaCan
Menu
Back to cohort
Record W2411290619

Measures of language outcomes using the Aboriginal Children's Survey.

2013· article· en· W2411290619 on OpenAlexaff
Leanne Findlay, Dafna Kohen

Bibliographic record

VenuePubMed · 2013
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsMilestoneConceptualizationLanguage delayLanguage developmentLanguage assessmentPsychologyPopulationFirst languageDevelopmental psychologyConstruct (python library)MedicineLinguisticsComputer scienceMathematics educationGeography
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Speech and language skills are an important developmental milestone for all children, and one of the most prevalent forms of developmental delay among Aboriginal children. However, population-based indicators of Aboriginal children's language outcomes are limited. DATA AND METHODS: Data from the Aboriginal Children's Survey (ACS) were used to examine measures of language for Aboriginal children who were 2 to 5 years of age. Responses to ACS questions on ability in any language were examined in exploratory factor analyses to determine possible language indicators. Construct validity was examined by regressing language outcomes onto socio-demographic characteristics known to be associated with children's language. RESULTS: Four language outcomes were identified and labelled: expressive language, mutual understanding, story-telling, and speech and language difficulties. INTERPRETATION: The conceptualization of items from the ACS into separate language indicators can be used by researchers examining young Aboriginal children's language outcomes.

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)0.003
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.546

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.038
GPT teacher head0.300
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 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

Citations3
Published2013
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicLanguage Development and DisordersFrench-language works237,207