Advancing Off-Site Construction: Assessing Organizational Maturity and Capabilities in the Canadian Construction Industry
Bibliographic record
Abstract
The construction industry, despite being a vital sector for societal growth, has struggled to match the accelerated growth seen in other peer industries. Over the past two decades, productivity within construction has remained stagnant, posing challenges to meeting societal demands and sustainability targets. Recognizing the potential of digitalization to revolutionize construction processes, this research addresses the critical need to assess and benchmark organizational maturity and capabilities in the Canadian construction industry, particularly in the context of off-site construction methodologies. This research project aims to establish a comprehensive method for assessing organizational capabilities related to off-site construction, thereby providing insights into current capacities and offering guidance for industry stakeholders to embrace advanced technologies and practices. Building upon an established international framework, the project evaluates organizational maturity across dimensions of people, process, and technology. By focusing on phases of design, manufacturing, and construction, the project will provide a nuanced understanding of the construction industry's readiness for off-site construction adoption. The project provides a conceptual framework to enable construction companies to evaluate their maturity levels relative to industry peers, considering factors such as geography, size, and organizational type. By facilitating this benchmarking process, the research fosters a culture of continuous improvement and innovation within the Canadian construction industry. Ultimately, the findings of this research will contribute to the advancement of off-site construction practices, enhancing productivity, sustainability, and overall industry performance.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".