Understanding the gender gap in school performance among low-income children
Bibliographic record
Abstract
Internationally, girls outperform boys in overall school performance. The gender gap is particularly large among those in at-risk groups, such as children from families at economic disadvantage. This study modeled the academic trajectories of a low-income sample of boys and girls from the Concordia Longitudinal Risk Project across the full course of schooling. Results from a multiple-group latent growth curve analysis revealed that children from this low-income sample demonstrated a significant decreasing trajectory of academic performance over time, which intensified after the transition from elementary to secondary schooling. A gender gap in academic performance emerged after the children transitioned to secondary school, with girls outperforming boys. Boys continued to experience greater academic decline than did girls across the secondary school years, and individual and family characteristics assessed in early elementary school predicted these academic trajectories. At school entry, boys showed higher levels of attention problems than did girls, which in turn predicted boys’ poorer school performance. However, boys with stronger reading skills and greater maternal school involvement during the early years of schooling were protected against declining academic performance across the secondary school years. Implications for prevention programs are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".