Producing sustainability professionals: Assessing graduate attributes in sustainability
Bibliographic record
Abstract
The 'Producing sustainability professionals: Assessing graduate attributes in sustainability study' developed a tool to identify how a sample of RMIT alumni apply RMIT's 'environmentally aware and responsible' graduate attribute (EAR GA) within their professional practice. This research sits within the broader graduate attributes project that has been undertaken across universities around the world (see Barrie 2012) and within research on sustainability and education, specifically understanding learning outcomes as a result of education and sustainability. A critical knowledge gap currently exists in the understanding of graduate learning outcomes and employability skills. Specifically, it is unclear how graduates are applying the attributes and skills developed through their degree programs, and if these are relevant in their workplaces. This project assessed the extent to which graduates understand, and can apply, sustainability attributes in the workplace. The project developed and evaluated a tool for the sector to aid assessment of sustainability attributes, and to inform learning and teaching strategies for addressing curriculum gaps identified through its application. The application of this tool provides a critical feedback loop to enable academics to understand how their teaching relates to the needs of employers and helps them to improve curriculum and graduate employability. The tool is applicable across the sector for the measurement of sustainability attributes in Australian university graduates, with potential application to graduate attributes in other areas.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".