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Record W2955535459 · doi:10.3386/w22267

Family Disadvantage and the Gender Gap in Behavioral and Educational Outcomes

2016· preprint· en· W2955535459 on OpenAlexaff
David Autor, David Figlio, Krzysztof Karbownik, Jeffrey Roth, Melanie Wasserman

Bibliographic record

VenueNational Bureau of Economic Research · 2016
Typepreprint
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversité du Québec à Montréal
FundersNational Institute on AgingSage FoundationNational Institute of Child Health and Human DevelopmentRussell Sage FoundationBill and Melinda Gates FoundationYrjö Jahnssonin SäätiöNational Science Foundation
KeywordsDisadvantagePsychologyGender gapDevelopmental psychologySociologyDemographic economicsComputer scienceEconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

Using birth certificates matched to schooling records for Florida children born 1992-2002, we assess whether family disadvantage disproportionately impedes the pre-market development of boys.We find that, relative to their sisters, boys born to disadvantaged families have higher rates of disciplinary problems, lower achievement scores, and fewer high-school completions.Evidence supports that this is a causal effect of the post-natal environment; family disadvantage is unrelated to the gender gap in neonatal health.We conclude that the gender gap among black children is larger than among white children in substantial part because black children are raised in more disadvantaged families.

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)0.003
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.273
GPT teacher head0.531
Teacher spread0.258 · 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

Citations73
Published2016
Admission routes1
Has abstractyes

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