Reduction of construction wastes by improving construction contract management: a multinational evaluation
Bibliographic record
Abstract
The Canadian construction industry generates 30% of the total municipal solid waste deposited in landfills. Ample evidence can be found in the published literature about rework and waste generation due to ambiguity and errors in contract documents. Also, the literature quotes that disclaimer clauses in contract documents are included in the contractual agreements to prevent contractor claims, which often cause rework. Our professional practice has also noted that there are several disclaimer clauses in standard contract documents which have the potential to cause rework (and associated waste). This article illustrates a comparative study of standard contractual documents and their potential to create rework (and associated waste) in different regions of the world. The objectives of this study are (1) to analyse standard contractual documents in Canada, the USA and Australia in terms of their potential to generate rework and waste, and (2) to propose changes/amendments to the existing standard contract documents to minimise/avoid rework. In terms of construction waste management, all the reviewed standard contract documents have deficiencies. The parties that produce the contract documents include exculpatory clauses to avoid the other party's claims. This approach tends to result in rework and construction waste. The contractual agreements/contract documents should be free from errors, deficiencies, ambiguity and unfair risk transfers to minimise/avoid potential to generate rework and waste.
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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 | 0.047 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".