Collaboration Enables Innovative Timber Structure Adoption in Construction
Bibliographic record
Abstract
Timber structures in construction have become more popular in recent years. Nevertheless, besides the complexity of designing, contracting and building these structures, a barrier to their market growth is the complexity of their supply chain relationships encompassing architects, engineers, builders and suppliers. The objective of this study is therefore to identify and characterize the supply chain relationships shared by these stakeholders within a massive timber construction project. Twenty-seven semi-structured interviews with architects, structural engineers, builders and timber element suppliers from nine countries, participant observations and secondary data were used to study the various relationship levels involved in timber construction projects. Triangulation and qualitative data analysis were also conducted. Three levels of relationships were then identified: “Contractual,” “Massive timber construction project” and “Massive timber construction industry development.” Results showed that timber structures involve value-added stakeholder relationships rather than linear relationships. These relationships appeared closer and more frequent and involved knowledge and information sharing. Furthermore, prefabricated systems allow for smoother relationships by limiting the number of stakeholders while promoting innovative thinking.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".