Developing graduate career readiness in Australia: Shifting from extra-curricular internships to work-integrated learning
Bibliographic record
Abstract
There is broad acknowledgement that higher education should produce career-ready graduates and the role of practical experience--such as part-time employment, unregulated extra-curricular internships and work-integrated learning (WIL)--in achieving this. WIL is critical for developing the non-technical skills, disciplinary expertise and career self-management competencies required to prepare graduates for the world-of-work. Although Australia appears committed to growing WIL, many employers engage in extra-curricular internships while there is a lack of industry partners available to meet student demand for WIL. Extra-curricular internships may, therefore, be considered the 'black market' to WIL and could be constraining the achievement of targeted growth in Australia's National Strategy for WIL. This paper highlights that extra-curricular internships may not be governed by the good practice principles critical to a quality work-based learning experience. It explores possible reasons for stakeholder preference for unregulated, extra-curricular internships and presents strategies to shift their engagement to WIL.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".