Evaluation of construction contract documents to be applied in modular construction focusing ambiguities; A text processing approach
Bibliographic record
Abstract
Modular coordination in building construction has become increasingly popular, particularly in Northern Europe and North America. In Canada, modular construction came to considerable attention over the last decade due to its valuable effect on project constraints, safety, and preventing construction and demolition waste. However, the modular construction industry still adopts the same administrative procedures designed for the conventional construction industry, even though the features of modular and conventional construction are different in terms of construction processes and methods. Due to this trend, ambiguities in administrative documents are widely occurred and are one of the main causes to generate conflict, disputes, and claims between owners and modular suppliers as general contractors. As a first step in the this research to overcome this challenge, the research team focuses on investigating the contents and structures of the current standard contracts and modular RFPs, which are one of the major sources of confusion in modular construction, in order to mitigate and/or remove the ambiguities based on the considering the specifications of off-site construction procedures and system. In this case, this research illustrates a conceptual framework that has two parts: First, classification of the main sources of ambiguities in construction contracts (both Conventional and modular) and second, to identify the similarities and differences between Canadian documents (standard contracts and modular RFPs) and benchmark countries by applying through text processing and readability analysis. We applied text processing to find top terms, including terms with high frequency (TF) in each document, also high TF-IDF terms, which species occur in one document and not others then, we detected manually the three standard contracts and four RFPs and compare them with the output of literature review to identify the major issues that are common. The readability analysis shows the textual complexity of a document and to what extent the documents are difficult to read. The main findings indicate that the modular industry in Canada suffers from a lack of specific standard contract documents for modular construction.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".