Maturity-based mapping of technology and method innovation in off-site construction: conceptual frameworks
Bibliographic record
Abstract
The construction industry has been associated with inefficiencies. In contrast, Off-Site Construction (OSC) is a modern method of construction that has demonstrated significant improvements over conventional on-site methods. Despite that, OSC represents a tiny portion of the construction industry with a limited rate of diffusion and acceptance. One reason for that is associated with the lack or immaturity of OSC-related research and innovation benchmarking. This benchmarking helps in expanding OSC implementation as a component in driving and directing OSC research as well as roadmapping and measuring the innovation advancements. Hence, this study was intended to contribute to the OSC benchmarking by mapping innovation that paves the road towards building a strategic research and innovation roadmap in OSC. Among different innovation types, this study is limited to two types: technology-oriented and OSC method-oriented innovation. Unlike the traditional roadmaps in the literature, the envisioned roadmap design for OSC innovation in this study is based on maturity modelling. This design includes four components: framework, maturity scales, benchmarks, and targets. However, the focus of the current stage is on the developing the mapping components (framework and maturity scales). Consequently, two sets of frameworks and maturity models were developed to realize the two identified innovation types in OSC. The applicability of these frameworks and scales was demonstrated through hypothetical examples and a case study that is limited to technology-oriented research in the Canadian context. Accordingly, the subsequent case study scope embraces the last three research community meetings (2015-2019) relevant to our study in the indicated context. Based on this case study, the framework was found easy to understand, simple to implement, scalable, applicable across different contexts, and facilitates capturing benchmarks and targets. This confirms promising benefits of the developed frameworks and their effectiveness in roadmapping OSC innovation.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".