Review of the policy context surrounding the digital transformation of the public construction industry in France and Quebec
Bibliographic record
Abstract
Building Information Modeling (BIM) is a digital process that enables the creation, management, and sharing of structured data throughout the lifecycle of construction projects, fostering efficiency, collaboration, and sustainability.In recent years, several governments have implemented national action plans to support BIM adoption, but with different timelines and strategies.France launched first the Plan de Transformation Numérique du Bâtiment and then Plan BIM, while Quebec introduced its Feuille de route gouvernementale pour la modélisation des données des infrastructures (2021)(2022)(2023)(2024)(2025)(2026).Although the literature has examined challenges related to BIM adoption, few studies have explored the impact of these policies and the diffusion dynamics they have generated.This study addresses this gap by comparing BIM policies in France and Quebec-two regions with different governance structures but strong institutional collaborations in construction and digital innovation.Through an in-depth review of recent literature and policy documents, we analyze the objectives, implementation strategies, and challenges associated with BIM policies.Our research focuses on four key questions: (1) the government's role in structuring digital transformation efforts; (2) how public policy influences BIM adoption at regional and national levels; (3) the role of standards in BIM policy development and dissemination; and (4) key features of policy frameworks supporting BIM implementation.This study provides valuable insights into how policy frameworks shape digital transformation in the construction sector.By comparing two distinct yet interconnected contexts, it offers lessons for other regions seeking to enhance their construction industry's digital capabilities and overall efficiency.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".