Examining the interplay of graduate attributes and a national skills passport in Australia: a tripartite scoping review
Bibliographic record
Abstract
Purpose The Australian Government has proposed a National Skills Passport (NSP) to provide a central digital source for an individual’s qualifications, certifications and skills. There is constant debate around the responsibility of universities to prepare graduates for the workforce not just with disciplinary competencies but also with skills directly applicable in an employment scenario. This paper aims to explore the role played by universities in articulating the non-technical skills of their students and consider their utility in a new NSP. Design/methodology/approach This scoping review encompasses academic literature, grey literature, expert interviews, and peer-to-peer dialogue in an interdisciplinary, innovative educative partnering of academic-industry-student. This methodology was adopted to broaden the remit of the research, and the recency of the Australian Government announcement necessitated outreach to understand stakeholder perspectives and shed light on areas not yet fully explored in academic literature. Findings The review finds that despite their ubiquity in Australian higher education, institution-stated ‘graduate attributes’ struggle to resonate with both students and industry. An NSP provides an opportunity for higher education institutions to reframe the content, disciplinary connection and usage of these attributes, delivering tangible buy-in from industry and amplifying the likelihood of adoption by students as the first users of the Passport. Originality/value This study brought together an industry organisation, the Universities Admissions Centre (“UAC”), the University of Sydney and its students in partnership to consider this complex problem in an experiential, interdisciplinary work-based learning environment. It is believed to be the first demonstrable output of academia-student-industry co-designed scholarly research on this topic, activating student agency in this manner, in Australia.
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 | 0.038 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".