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Record W3154136253 · doi:10.17848/978088099.ch4

Gender Differences in (Some) Formative Inputs to Child Development

2022· preprint· en· W3154136253 on OpenAlexfundaboutno aff
Michael Baker

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchUniversity of TorontoWestern Michigan UniversityInstitut für Arbeitsmarkt- und BerufsforschungW.E. Upjohn Institute for Employment Research
KeywordsDemographyPsychologyDevelopmental psychologyGender gapLow birth weightDeveloping countryDepression (economics)MedicineDemographic economicsEconomicsPregnancySociologyEconomic growth

Abstract

fetched live from OpenAlex

While there is a large literature on gender differences in important childhood developmental inputs in developing countries, the evidence for developed countries is relatively limited. I investigate gender differences in some of these inputs in the US and Canada. In the US very low birthweight males face excess mortality compared to their female counterparts. I provide evidence that the previously documented increase in mortality with the withdrawal of critical care at the Very Low Birth Weight (VLBW) threshold is primarily for boys. The fact that the critical care of both boys and girls changes discretely at this threshold suggests a possible misallocation of scarce hospital resources. In the US first born girls are breastfed longer than first born males, but the difference is so small that it is unlikely to have any consequence. Finally, mothers in the US and Canada are more likely to experience depression post birth when the first born child is a boy. Perhaps related, the parenting of first born boys in Canada in the first years of life is more likely to be confrontational.

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)0.001
metaresearch head score (Gemma)0.008
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 score0.024
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0180.001

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 head0.035
GPT teacher head0.300
Teacher spread0.265 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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