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Record W2277947026 · doi:10.3386/w19331

Leaving Boys Behind: Gender Disparities in High Academic Achievement

2013· report· en· W2277947026 on OpenAlexafffund
Nicole M. Fortin, Philip Oreopoulos, Shelley Phipps

Bibliographic record

VenueNational Bureau of Economic Research · 2013
Typereport
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsDalhousie UniversityUniversity of TorontoUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institute for Advanced Research
KeywordsAcademic achievementPsychologyDemographic economicsDevelopmental psychologyEconomics

Abstract

fetched live from OpenAlex

Using three decades of data from the "Monitoring the Future" cross-sectional surveys, this paper shows that, from the 1980s to the 2000s, the mode of girls' high school GPA distribution has shifted from "B" to "A", essentially "leaving boys behind" as the mode of boys' GPA distribution stayed at "B". In a reweighted Oaxaca-Blinder decomposition of achievement at each GPA level, we find that gender differences in post-secondary expectations, controlling for school ability, and as early as 8th grade are the most important factor accounting for this trend. Increases in the growing proportion of girls who aim for a post-graduate degree are sufficient to account for the increase over time in the proportion of girls earning "A's". The larger relative share of boys obtaining "C" and C+" can be accounted for by a higher frequency of school misbehavior and a higher proportion of boys aiming for a two-year college degree.

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)0.002
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 score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.477
GPT teacher head0.556
Teacher spread0.078 · 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

Citations153
Published2013
Admission routes2
Has abstractyes

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