“Who Has to Work Harder, Girls or Boys?” Children's Gender Stereotypes About Required Effort in Math and Reading
Bibliographic record
Abstract
Our culture attributes women's and girls' ability in mathematics and related domains to their efforts more so than men's and boys'-a stereotype that contributes to inequities in scientific and technical careers. Here, we provide the first investigation of this gender stereotype in children, examining its endorsement across a broad age range and assessing its links to student motivation. Specifically, we investigated 6- to 12-year-old US elementary school students' stereotypes about how hard girls and boys have to work to be good at math and, as a comparison, reading (N = 246; 50% girls; 50% White, 19% Asian, 9% Multiracial, 6% Black). We also tested whether these stereotypes are related to children's self-efficacy, interest, and anxiety in math and reading, and whether these links differ in strength across age. Although we anticipated that, like US adults, children would stereotype girls as having to work harder than boys to be good at math, we found that-in line with previously documented gender ingroup biases-younger children reported that effort was less of a requirement for their own (vs. another) gender; this ingroup bias was absent among older children. However, consistent with our hypotheses, children who more strongly believed their own gender needed to work harder to be good in a subject also reported lower self-efficacy in that subject, and older children reported lower interest in it as well. The present research contributes to our understanding of how to effectively encourage student motivation in school.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".