Editorial du Numéro 4 de la revue DNArchi : Cartographier la diversité des pratiques numériques pour l’architecture : observations, réflexions et méthodes
Bibliographic record
Abstract
Computer-Aided Design (CAD) and Drafting (CADD) tools, as we know them today, became commercially available and accessible to architecture firms as early as the beginning of the 1980s. However, it was not until the late 1990s that their use became widespread. This large-scale digitalization is now more than twenty years old. The transformation of professional practices driven by the development of digital technologies has previously been documented and linked to the evolving roles of architects, the organization of architecture firms, their workflows, and their output. Today, as the digitalization of the construction sector and the practice of architecture continues, the aim of this issue of *DNArchi* is to assess these changes and offer insights into the evolving practices and modes of architectural production shaped by digital advancements.For this issue, we received 15 article proposals from authors across five French-speaking countries: France, Canada, Tunisia, Belgium, and Luxembourg. Two types of contributions were submitted: scientific articles, peer-reviewed in a double-blind process by at least two reviewers, and narrative contributions by professionals from diverse backgrounds (teachers, architects, engineers) sharing their experiences. This issue includes nine articles in total—five scientific papers, four narrative accounts—as well as a book review.Three main themes naturally emerged in this issue. The first, **"Observing the Evolution of Practices,"** focuses on documenting both past changes and potential future developments in digital practices among industry professionals. The second, **"Mapping? A Question of Method,"** questions the very notion of methodology in mapping practices. The third, **"Teaching: When Digital Tools Transform the Way Architecture Is Taught,"** connects the evolution of tools and professional practices with shifts in architectural education, inviting reflection on the necessary skills and teaching methods.We are especially pleased to publish this issue and warmly thank the authors and reviewers who contributed high-quality work. Through this edition, *DNArchi* positions itself as a key player in the field of scientific publishing on the topic of digital technologies in relation to architecture across the international Francophone community.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".