An integrated methodology for collecting, classifying, and analyzing Canadian construction court cases
Bibliographic record
Abstract
Construction contracts are becoming more complicated, and the increase in complexity of construction processes, documents, and conditions of contracts has contributed to a higher possibility of disputes and conflicting interpretations. The judicial system has been the means for dispute resolution for claims that cannot be solved through other means such as negotiation and arbitration. Knowledge of previous outcomes of judicial processes will both inform participants in a dispute and increase the likelihood of a less-expensive out-of-court dispute-resolution process. This paper presents a methodology to classify, categorize, and analyze Canadian case-law construction claims. In total, 567 Canadian construction court cases have been collected from 10 different sources and are classified into 12 categories that follow the Canadian Construction Documents Committee (CCDC) standard construction contract document CCDC 2-1994. The proposed methodology is implemented in a computer-integrated system called the Canadian construction claim tracker (CCCT), which consists of one central database and three modules, namely a statistical module, a prediction module, and a classification module. The CCCT provides its users with easy and quick access to past case-law claim information.Key words: construction courts, claims, litigation, artificial neural networks, Canadian Construction Documents Committee.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".