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Record W4383710793 · doi:10.1111/cdev.13967

Mechanisms and pathways linking kindergarten behavior problems with mid-life employment earnings for males from low-income neighborhoods

2023· article· en· W4383710793 on OpenAlexafffund
Francis Vergunst, Frank Vitaro, Mara Brendgen, Marie‐Pier Larose, Alain Girard, Richard E. Tremblay, Sylvana M. Côté

Bibliographic record

VenueChild Development · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersFonds de Recherche du Québec - SantéSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchFonds de recherche du QuébecSocial Sciences and Humanities Research CouncilCanada Foundation for Innovation
KeywordsEarningsPsychologyPsychosocialAggressionEducational attainmentDevelopmental psychologyGraduation (instrument)CohortSocioeconomic statusDemographyPsychiatryPopulationEconomicsMedicine

Abstract

fetched live from OpenAlex

Childhood behavior problems are associated with reduced labor market participation and lower earnings in adulthood, but little is known about the pathways and mechanisms that explain these associations. Drawing on a 33-year prospective birth cohort of White males from low-income backgrounds (n = 1040), we conducted a path analysis linking participants' teacher-rated behavior problems at age 6 years-that is, inattention, hyperactivity, aggression-opposition, and low prosociality-to employment earnings at age 35-39 years obtained from tax records. We examined three psychosocial mediators at age 11-12 years (academic, behavioral, social) and two mediators at age 25 years (non-high school graduation, criminal convictions). Our findings support the notion that multiple psychosocial pathways-especially low education attainment-link kindergarten behavior problems to lower employment earnings decades later.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.265
Teacher spread0.238 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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