Differential Effects of Adolescents’ Expectancy and Value Beliefs about Math and English on Math/Science-Related and Human Services-Related Career Plans
Bibliographic record
Abstract
Informed by Eccles’s expectancy-value theory and Moller and Marsh’s dimensional comparison theory, this study examined the effects of adolescents’ motivational beliefs across two academic domains, English and Math, on adolescents’ math/science-related and human services-related career plans at the end of high school (N = 425). Consistent with prior evidence, male adolescents were more likely to aspire to math/science-related careers, whereas female adolescents favored human services occupations. The effects of gender on these career plans were mediated by adolescents’ valuing of English. Compared to males, females were less likely to consider math/science-related careers and more likely to consider human services occupations partially because they valued English more than did males. In addition, a negative interaction effect suggested that adolescents’ math-related self-concept of ability was a weaker predictor of math/science-related career plans at higher levels of perceived ability in English. Accordingly, the combination of high perceived ability in both math and English implied a somewhat lower probability of pursuing math/science-related careers, relative to individuals with high math, but lower English self-concept of ability. These findings underscore the importance of considering cross-domain influences in the career choice process, and especially with regard to gendered choices in the domains of math and science.
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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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".