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Record W2056632839 · doi:10.1177/082957350401900106

The Impact of Junior Kindergarten on Math Skills in Elementary School

2004· article· en· W2056632839 on OpenAlexaff
Linda S. Pagani, Denis Larocque, Richard E. Tremblay, Pierre Lapointe

Bibliographic record

VenueCanadian Journal of School Psychology · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDisadvantagedAttendancePsychologyMathematics educationDisadvantageCurriculumDevelopmental psychologyPrimary educationCognitionCognitive developmentPedagogy

Abstract

fetched live from OpenAlex

Using data from the first cycle of the National Longitudinal Survey of Children and Youth, this study examines the impact of junior kindergarten on children's later skills in math, above and beyond regional differences and individual/ household factors. It was hypothesized that earlier schooling would better prepare children for first formal learning of arithmetic, resulting in a more developed knowledge-base for optimal performance at the middle and end of elementary school. We also hypothesized that junior kindergarten attendance would reduce the performance gap between children from economically disadvantaged and those from advantaged families. Our results suggest that earlier schooling did not provide the cognitive boost that would lead to better performance in arithmetic, especially for girls. These results are above and beyond a number of controls (sex, age, region, SES, family functioning, family configuration, education, and family size). Junior kindergarten attendance did not help bridge the performance gap based on economic advantage/disadvantage. Primary school children from poor families who attended junior kindergarten did not perform on a par with their middle-class peers. This study makes an important case for enrichment in the central conceptual structures related to intuitive and informal mathematical knowledge through games and activities potentially featured in the junior kindergarten curriculum.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.284
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0010.000
Research integrity0.0000.001
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 head0.025
GPT teacher head0.373
Teacher spread0.348 · 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

Citations4
Published2004
Admission routes1
Has abstractyes

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