Gendered motivational processes affecting high school mathematics participation, educational aspirations, and career plans: A comparison of samples from Australia, Canada, and the United States.
Bibliographic record
Abstract
In this international, longitudinal study, we explored gender differences in, and gendered relationships among, math-related motivations emphasized in the Eccles (Parsons) et al. (1983) expectancy-value framework, high school math participation, educational aspirations, and career plans. Participants were from Australia, Canada, and the United States (Ns = 358, 471, 418, respectively) in Grades 9/10 at Time 1 and Grades 11/12 at Time 2. The 3 samples came from suburban middle to upper-middle socioeconomic backgrounds, primarily of Anglo-European descent. Multivariate analyses of variance revealed stereotypic gender differences in educational and occupational outcomes only among the Australian sample. Multigroup structural equation models identified latent mean differences where male adolescents held higher intrinsic value for math in the Australian sample and higher ability/success expectancy in both North American samples. Ability/success expectancy was a key predictor in the North American samples, in contrast to intrinsic value in the Australian sample. Attainment/utility ("importance") values were more important for female adolescents' career choices, except in the Australian sample. Findings are interpreted in relation to gender socialization practices, degree and type of early choice, and specialization across settings. Implications are discussed for long-term math engagement and career selection for female and male adolescents. (PsycINFO Database Record (c) 2012 APA, all rights reserved).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".