Building Information Modeling in Quebec’s Procurement for Public Infrastructure: A Case for Integrated Project Delivery
Bibliographic record
Abstract
The Province of Quebec is currently in the process of adopting building information modeling (BIM) for major infrastructure projects. However, legal and contractual concerns such as the tendering process, adjudication criteria, intellectual property and risk–reward sharing mechanisms hinder the implementation of an efficient BIM process. This paper addresses the following question: How do norms, whether legislative, regulatory or contractual, functionally or dysfunctionally affect the effective implementation of BIM in Quebec’s public infrastructure framework? This paper suggests that the use of Integrated Project Delivery (IPD) should help mitigate legal barriers hindering BIM implementation, while preserving balance between fairness and encouraging collaboration. Quebec’s normative framework, which includes legislation, regulations, contracts and infra-regulatory rules, should be modified to standardize collaborative mechanisms, integrate two-stage negotiated processes such as rank-and-run or best and final offer and enable the assessment of tenderers’ objective qualities and more subjective qualities. Furthermore, a risk–reward sharing mechanism should be implemented through target costing, and upstream participation from a wide range of stakeholders should be encouraged.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".