Designing and mapping a generic attributes curriculum for science undergraduate students: a faculty-wide collaborative project
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite much emphasis in the recent literature (including a special issue of Higher Education \nResearch and Development in 2004), the implementation of generic attributes curricula have been \npatchy, both within and between universities worldwide (Barrie 2006; Jones 2002; Drummond, \nNixon and Wiltshire 1998). However, the benefits of explicitly incorporating generic graduate \nattributes into the undergraduate curriculum are widely recognised (see reviews by Barrie 2006; \nJones 2002): the identification of generic graduate attributes should focus the planning, \nimplementation and evaluation of curricula by faculties and schools so that teaching and learning \nstrategies and assessment activities reflect a commitment to supporting students to achieve generic \nskills and capabilities, as well as discipline-related knowledge and skills. As a result, students will be \nbetter prepared for the workplace, having developed a broad range of capabilities such as problemsolving, \ncritical evaluation and teamwork in addition to discipline-related expertise (Candy 2000).
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it