Litigation management process in construction industry
Bibliographic record
Abstract
For an Engineering, Procurement and Construction Management contract, collaboration between the different actors is essential from the very beginning of the project to consider all the constraints. Working upstream reduces the occurrence of problems that could lead to claims. As long as trust and dialogue are present, disputes can be settled by agreement, but if dialogue is cut off, the negotiation phases are over. The solution is then to move to alternative dispute resolution methods involving outside third-party mediator. If, despite this, no agreement is reached, the last option is to proceed to legal proceedings. This paper develops a litigation management process for the construction industry in Quebec (Canada) to guide future litigation project managers, whether they are on the plaintiffs of the defendant's side of the claim. The proposed process links the litigation team members, lawyers and experts. The process divided into ten phases, contains sequences of activities, resources, input and output documents and deliverables. The process was validated in terms of standardization in order to assess its capability to support different construction types of projects and contracts. With this process, litigation managers will be able to oversee litigation through a better visibility of the activities to be planned and forecast costs.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".