Mapping of Circular Construction Ecosystems’ Characteristics: Interconnections, Relationships, and Synchronization of Stakeholders at the Micro, Meso, and Macro Scales
Bibliographic record
Abstract
The application of circular strategies in the architecture, engineering, construction, and operations (AECO) sector has been extensively researched, demonstrating the significance of technical approaches. However, research also focuses on the organizational challenges that arise within circular networks. Recent studies emphasize the importance of collective action in fostering cooperation across the value chain to achieve circular economy (CE) goals. Nevertheless, a considerable amount of research, including EU policies, tends to concentrate on “end-of-pipe” solutions, while failing to adequately address the socio-ecological challenges inherent in the transition to a CE. This study aims to explore collective activities and work in circular construction ecosystems at the macro, meso, and micro scales, identifying their interconnections. The findings of the literature review indicate that a successful transition to a CE requires a deeper commitment from stakeholders, which is influenced by the structure and relationships within the ecosystem. The increasing complexity of these ecosystems necessitates a redefinition of stakeholder roles and competencies, emphasizing a collective perception of value. Given the lack of tools and research on collaboration, we propose developing a map of circular construction ecosystems to improve the visualization and understanding of their dynamics.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".