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Record W6901696713 · doi:10.60692/hxt8k-gjm72

Differences and secular trends in childhood IQ trajectories in Guatemala City

2020· article· en· W6901696713 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2020
Typearticle
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsSickKids FoundationHospital for Sick Children
FundersMedical Research Council
KeywordsIntelligence quotientDisadvantagedSocioeconomic statusSecular variationInequalityLongitudinal studyMultilevel modelEarly childhoodChild development

Abstract

fetched live from OpenAlex

This study documents differences in childhood IQ trajectories of Guatemala City children, aged 6–15 years and born 1961–1993, according to school attended, height-for-age Z-scores (HAZ) and over time (Flynn effect). IQ data come from the Universidad del Valle de Guatemala Longitudinal Study of Child and Adolescent Development. IQ was measured using standardised tests from the Otis-Lennon Mental Ability Test-series. A multilevel model was developed to describe 60,986 IQ observations (level 1), in 22,724 children (level 2), in five schools representing students of different socioeconomic status (SES) (level 3). Average IQ trajectories differed by school. The difference in average IQ at age 11 years between the students of high and low SES schools was 28.7 points. A one-unit increase in HAZ was associated with a 1.42 (0.72, 2.11) unit higher IQ if HAZ was <0, this association was stronger in public compared to private schools. Conversely, one unit increase in HAZ was only associated with a 0.3 (0.001, 0.5) unit higher IQ if HAZ was ≥0. With each birth year increase, IQ at age 11 years increased by 0.14 (95% CI 0.12, 0.16) units, although this Flynn effect attenuated slightly across adolescence. We found no evidence of secular change in the inequality in IQ trajectories (according to school or HAZ). Shorter children from disadvantaged schools in Guatemala City have lower IQ than their taller and wealthier peers, possibly reflecting the damaging effects of poor early life environments both for linear growth and cognitive 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 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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.448

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)0.0000.000

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 head0.057
GPT teacher head0.247
Teacher spread0.190 · 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.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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