Do Boys and Girls Use Computers Differently, and Does It Contribute to Why Boys do Worse in School Than Girls?
Bibliographic record
Abstract
Abstract Boys are doing worse in school than are girls, which has been dubbed “the Boy Crisis.” An analysis of the latest data on educational outcomes among boys and girls reveals extensive disparities in grades, reading and writing test scores, and other measurable educational outcomes, and these disparities exist across family resources and race. Focusing on disadvantaged schoolchildren, I then examine whether time investments made by boys and girls related to computer use contribute to the gender gap in academic achievement. Data from several sources indicate that boys are less likely to use computers for schoolwork and are more likely to use computers for playing games, but are less likely to use computers for social networking and email than are girls. Using data from a large field experiment randomly providing free personal computers to schoolchildren for home use, I also test whether these differential patterns of computer use displace homework time and ultimately translate into worse educational outcomes among boys. No evidence is found indicating that personal computers crowd out homework time and effort for disadvantaged boys relative to girls. Home computers also do not have negative effects on educational outcomes such as grades, test scores, courses completed, and tardies for disadvantaged boys relative to girls.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".