Children’s math and science beliefs about underrepresented peers are related to STEM occupation expectations
Bibliographic record
Abstract
• Math and science competence beliefs predict expectations about who will grow up to be a doctor. • Children are more likely to expect a girl to grow up to be a doctor than to be a scientist. • Girls are more likely than boys to expect a girl would grow up to be both a scientist and a doctor • Age-related increase for believing that an URM child will grow up to be a scientist. Children’s interest and motivation in math and science decline dramatically beginning as early as elementary school (K-5). This is especially true for marginalized students, such as girls and children from underrepresented racial-ethnic minority (URM) backgrounds. Understanding the relation between children’s STEM (science, technology, engineering, and math) competence beliefs and STEM occupation expectations provides a basis for timely and targeted intervention. This association is crucial because expectations about who will pursue and engage in STEM occupations reveals potential biases that might translate into exclusion of participation from STEM-related activities in childhood. To examine this topic, a survey was administered to N = 842 children ages 7–12 years from different racial-ethnic backgrounds in the suburbs of a large Mid-Atlantic city. As hypothesized, we found that math and science competence beliefs about girls predicted children’s expectation that a girl, rather than a boy, would grow up to be a scientist and a doctor. Further, math and science competency beliefs about URM peers predicted children’s expectation that a Black or Latine child would grow up to be a doctor, though these beliefs were not related to their expectations that a Black or Latine child would grow up to be a scientist. Additionally, participants were more likely to expect a girl to grow up to be a doctor than to be a scientist. The effects of participant age, gender, and race were also investigated. These findings contribute to understanding how best to broaden participation in math and science fields for all children.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".