Global Stakeholder Perceptions of Circular Building Adaptability, Open Building Methodology and Mass Timber Construction: A Qualitative Study
Bibliographic record
Abstract
To meet global carbon emissions reduction targets we need to adopt sustainable construction practices and transition toward a Circular Economy (CE). The present paper proposes a combination of CE-focused technologies, Circular Building Adaptability (CBA), Open Building (OB) and Mass Timber Construction (MTC) working in concert to support emissions reduction objectives. A total of 158 participants aged between 18 and 65+, from Europe (20%), North America/Canada (32%), South America (3%), Africa/South Africa (3%), Middles East (3%), Asia Pacific (36%) and Southeast Asia & Asia (45) provided open text-based reponses to survey items as part of a larger project. The sample predominately consisted of those who identified as male (87%) and worked as Engineers primarily from the Asia Pacific region and North America/Canada. The paper aimed to assess international construction industry stakeholders’ perceptions of CBA, OB and MTC in delivering a more sustainable construction solution for our built environments. Using a Braun and Clarke (2006) Thematic Analaysis, applying a pragmatist lens, themes interpreted from the data coalesce toward the primary objective of achieving carbon reduction and mitigating the negative environmental impacts of construction activities in the built environment. Themes included, Cost, Economic Barriers, and Market Dynamics Perceptions, Knowledge Gaps, and Education Sustainability, Carbon Reduction, and Environmental Impact, Regulatory and Safety Concerns, Adaptability and the Circular Economy, Open Building Approach and Social Implications. Evidence from the study indicates support for the concepts with participants articulating concerns regarding the adoption of novel construction solutions. The outcomes are discussed, and future research recommendations are offered.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".