A Goal-Oriented Framework for Implementing Change in Off-Site Construction in the Industry 4.0 Era
Bibliographic record
Abstract
With the trend of increasing automation and digitization, characteristic of the paradigm shift referred to broadly as Industry 4.0, the off-site construction sector is racing to keep pace with other industrial sectors in technological adoption and transformation. Although the rate of adoption is still relatively low, those off-site construction enterprises often proceed directly to investing in technologies before carefully identifying their needs and thoroughly analyzing and understanding their current state of operations. In this context, this study presents a six-step framework that will support decision makers in the following three areas: (1) understanding and evaluating the current state of their business operations; (2) identifying whether a new technology would be valuable to their business; and (3) properly implementing the technology, if needed. The framework was developed using a design science research approach and inductive reasoning. We briefly present preliminary observations based on an ongoing application of the proposed framework at an off-site construction company.
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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 | 0.028 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| 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".