Measuring Career Aspirations in Science, Technology, Engineering, Mathematics and Education
Bibliographic record
Abstract
Abstract There has been a sustained interest in student perceptions about STEM fields and their choice of careers over the past few decades. Research has shown that there is a decline in students pursuing STEM careers, and this has raised global concern. Despite these issues, no unistructural, broad, parsimonious and unambiguous quantitative instrument exists to probe student career aspirations. This paper highlights the background, extension and validation of an instrument, derived from a previous science-focussed high-quality instrument that allows student career aspirations to be quantitatively characterised. Participants were 1221 undergraduate students, 1003 of whom were judged to have provided good data, from 18 tertiary institutions in the USA and Canada. The resultant instrument is a reliable 20-question survey representing five clearly demarcated domains: Science, Technology, Engineering, Mathematics and Education. Each scale possesses high reliability (Cronbach’s alpha > 0.95), and high construct validity as determined by comparisons with their stated choices of career.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".