Developing engagement and literacy in science: What do the girls say?
Bibliographic record
Abstract
Despite decades of sustained national focus in several countries (e.g., Australia, Canada, New Zealand, UK, and USA) recent trends in students’ course-taking and career choices suggest proportionally fewer students pursuing STEM-related study. Consequently, to address this trend, it is important to better understand factors currently related to students’ engagement, literacy and attainment in STEM subjects and vocations. Our own recent research has examined students’ science literacy and engagement in association with formal (school-based) and informal (outside of school, home-related) factors, using retrospective analysis of Programme for International Student Assessment (PISA) data. In this study we purposefully recruited several female students enrolled in late-secondary school Physics. This selection meant that all participants were engaged in school science and likely to be considering post-secondary study in STEM, and possibly STEM-related careers. Our purpose was to hear from this select group of female science students, their stories of influences in the development of their engagement and literacy in science. In particular, we were interested in juxtaposing their stories against the explanatory regression models we had previously developed. In this way, our purpose was to test the nomothetic explanations previously offered using idiographic stories of factors related to the engagement of girls in 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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".