Evaluating the impact of public policies supporting digital transformation in the construction sector: towards a systematic evaluation framework
Bibliographic record
Abstract
Digital transformation in the public construction sector has given rise to major policy initiatives, such as France’s Digital Transition Plan for the Construction Industry (PTNB), the BIM Plan 2022, and Quebec’s Roadmap for built asset information modeling. These initiatives reflect a growing political commitment to fostering digital transformation across the industry. However, their actual impacts remain largely under-evaluated. This lack of evaluation can be partly explained by the relative novelty of these programs and the fact that only now—at a critical juncture where Building Information Modeling (BIM) practices are maturing—do we have sufficient hindsight and data to assess their outcomes meaningfully. This article is part of a broader research project aimed at developing robust approaches for evaluating public policies supporting digital transformation in construction. The present study focuses specifically on the methods available for assessing policy impacts and seeks to contribute to a better understanding of how to evaluate these transitions effectively. The paper begins by reviewing the main typologies of public policies associated with digital transformation, in order to clarify the diversity of policy instruments and intervention logics in this domain. It then proposes a critical analysis of existing policy evaluation methods—ranging from cost-benefit analysis to theory-based and mixed-method approaches—and assesses their relevance to the construction sector. Drawing on these insights, the paper proposes a methodological framework designed to guide future evaluations, while accounting for the specific challenges of the sector, such as fragmentation, long project cycles, and the hybrid nature of public-private partnerships.
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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 | 0.494 | 0.454 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.026 | 0.018 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".