Benchmarking International Best Practices for Circular Skills Training: Lessons for Australia
Bibliographic record
Abstract
Managing construction and demolition waste poses a persistent challenge necessitating effective management strategies. Circular economy principles have been acknowledged as pivotal for addressing this challenge, demanding a profound reassessment of design, construction, and maintenance practices, particularly focusing on material utilisation. Consequently, it is crucial to implement targeted training programs tailored for construction professionals. Furthermore, there exists a compelling rationale for integrating circular design thinking principles into academic curricula, acknowledging a critical skills gap in Australia. However, effective strategies to address this challenge remain an ongoing pursuit. This study aims to identify global best practices for advancing circular skills by conducting a systematic review of extant literature, specifically focusing on circular economies. Notably, the review emphasised practices observed in the Netherlands, Denmark, Japan, United Kingdon, United States, Finland, and Canada. The analysis delved into the types of CE skills and the different modes of delivery. The study’s findings serve to identify successful approaches and extract valuable insights from international contexts, which could be pertinent to Australia. Ultimately, this study provides evidence and recommendations to inform the development of Australia’s national circular skills strategy, retraining initiatives, and the seamless integration of circular principles into vocational and higher education curricula. Additionally, it contributes significantly to the global understanding of optimal practices for facilitating workforce transitions.
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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 | 0.086 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".