When Do Gender Differences in Academic Achievement Originate? Examining Preschoolers and Early School Children Longitudinally
Bibliographic record
Abstract
The skewed emphases on central tendency and dispersion statistics often provide an estimated summary of scores and variances of the overall distribution. Studies may therefore overlook significant variations across these distributions' different percentiles. This study examined gender academic disparities in STEM and reading subjects of the USA sample (Early Childhood Longitudinal Study-Kindergarten, ECLS-K:2011). The Quantile Regression (QR) model was utilized and found academic gender differences across school subjects, students’ academic grades, and proficiency levels. There were often more differences in the extreme tails of the distribution than around the mean. The gender gap started with the top students in kindergarten and quickly spread throughout the distribution and primary school grades. Boys at the extreme ends of the distribution had the lowest reading scores by a significant margin. However, boys consistently rank among the top students in math and science. Early-age attention and intervention are needed to avert subsequent-grade academic achievement inequalities.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".