Science, Technology, Engineering and Math Readiness: Ethno-linguistic and gender differences in high-school course selection patterns
Bibliographic record
Abstract
The study examines science-related course choices of high-school students in the culturally diverse schools of the province of British Columbia, Canada. The analysis employs K-12 provincial data and includes over 44,000 students born in 1990 who graduated from high school by 2009. The research sample reflects the presence of about 27% of students for whom English is not a first language. We construct an empirical model that examines ethno-linguistic and gender differences in Grade 12 course choices while accounting for personal and situational differences among students. The study employs a course selection typology that emphasizes readiness for science, technology, engineering and math fields of study. Findings indicate that math- and science-related course selection patterns are strongly associated with ethnicity, qualified not only by gender and prior math and science achievement but also by the individual's grade level at entry to the system and enrollment in English as a Second Language program. Students who are more likely to engage in math and science courses belong to Asian ethno-linguistic groups and entered the provincial school system during the senior high-school years. We suggest that ethnic diversity and broader academic exposure may play a crucial role in changing the gender composition of science classrooms, university fields of study and science-related occupations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".