“Who Has to Work Harder, Girls or Boys?” Children's Gender Stereotypes About Required Effort in Math and Reading
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it