EDUCATING FOR A CIRCULAR FUTURE: DIGITAL INNOVATIONS, INTERDISCIPLINARY LEARNING, AND GLOBAL POLICY INSIGHTS IN INDUSTRIAL SYMBIOSIS
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
Adapting to a Circular Economy (CE) within the context of ongoing digital transformation calls for a significant rethinking of educational models. Future professionals must be prepared with technical knowledge and the ability to think in systems and adapt to emerging digital environments. This research tends to analyse how different digital tools (i.e., digital platforms, blockchain, digital twins, the Internet of Things, and artificial intelligence) can be effectively integrated into educational practices supporting industrial symbiosis (IS) development. Beyond improving operational workflows and resource exchange, these technologies also serve as pedagogical tools that can deepen learners� understanding of complex sustainability issues. Despite their potential, various challenges hinder widespread adoption. These include the technical intricacies of digital tools, issues of standardisation and compatibility, and broader concerns about infrastructure and social acceptance. As such, the research highlights the importance of embedding digital competence, interdisciplinary learning, and hands-on educational strategies into higher and vocational training. In addition, the study examines global legislative achievements, comparing policy frameworks across the European Union, Canada, the Southern Americas, China, and Vietnam. This comparison reveals how regulatory and institutional cooperation and best practice assessment can drive the adoption of CE and IS principles at scale. Spatial planning and legislative incentives enable such developments and cannot be neglected. The research proposes an integrated educational and policy model to promote a more inclusive, coordinated, and effective shift toward a circular and digitally enabled economy.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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