When stereotypes disadvantage boys: Strength of stereotypes in mathematics and language arts and their relations with grades
Bibliographic record
Abstract
Abstract There is growing concern about boys' lagging performance in school, not only in language arts, where the gap is particularly pronounced, but also in mathematics. Stereotypes associating one gender with language arts or with mathematics are likely to contribute to these gaps. Such stereotypes can translate into explicit beliefs such as the extent to which students are aware of societal stereotypes or the extent to which they personally believe stereotypes to be true, but also indirectly into performance following a stereotype threat manipulation. However, few studies have considered these multiple stereotype expressions in both mathematics and language arts to examine their importance in predicting boys' and girls' actual grades in school. To fill this gap, two complementary studies examined high school boys' and girls' awareness and endorsement of stereotypes about both language arts ( n = 299) and mathematics ( n = 243), as well as whether stereotype threat impaired boys' performance on a spelling test. Although the effect of stereotype threat was not significant overall, our results showed that students were aware of and endorsed strong stereotypes advantaging girls in language arts. In mathematics, students endorsed counter‐traditional stereotypes slightly advantaging girls. Our results also showed that these multiple expressions of stereotypes related to students' grades. In doing so, our work provides insights regarding possible targets for interventions to reduce gender gaps disadvantaging boys in school.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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) | 0.004 | 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".