Bibliographic record
Abstract
Young professionals with STEM skills are in high and increasing demand. Unfortunately, there is a prevalent gender disparity among graduates in engineering and physics. Girls are opting out of studying physics before the end of year 10: less than one quarter of the year 11 and 12 physics cohort in NSW is female. Physics trained high school teachers are needed to engage students in junior secondary science. However, there is currently a state, national and global shortage of such teachers, which is particularly acute in regional schools. Having a conceptual focus and contextualising material has been shown to have a positive impact on students’ “physics identity” and consequently their interest in a STEM career. Teachers need a good understanding of physics themselves in order to design engaging classes for students. To address this shortage, UNSW has introduced an online Graduate Certificate in Physics for Science Teachers, which is now in its third year. Feedback from graduandates has been very positive: some have secured jobs in regional schools, many have commented on the impact it has had on their teaching of junior science, and some have shared resources they developed with colleagues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
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".