Évaluation d’un modèle de lien entre les niveaux micro et macro d’adoption du BIM
Bibliographic record
Abstract
Le BIM est considéré comme un ensemble de technologies de rupture qui doit entraîner une reconfiguration du travail dans l’ensemble de l’industrie. Cependant, peu de recherche s’est penché sur un cadre multi-échelle pour faire face à ce défi. Cette recherche consiste en une analyse empirique d’un modèle d'adoption multi-échelle du BIM de Kassem & Ahmed (2022) appliqué au secteur québécois de la construction. Ce modèle, la feuille de route gouvernementale et le diagnostic IQC4.0 s’inspirent des mêmes outils leurs influences sont examinées à travers l'étude de cas de deux donneurs d'ouvrages publics. Comme contribution à la théorie, cette recherche a permis de montrer la pertinence du cadre de Kassem et Ahmed et de proposer des améliorations pour mieux gérer les influences réciproques entre les niveaux macro et micro d’adoption du BIM. Sur le volet de la pratique, certaines carences sont mises en évidence sur le modèle macro du diagnostic IQC4.0 et ses liens avec la feuille de route gouvernementale.
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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 | 0.016 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".