Redefining Minimalism in Housing: Introducing the M1-13 Evaluation Framework via NLP and Multi-Scale Criteria
Bibliographic record
Abstract
This study redefines architectural minimalism in contemporary housing not as a visual trend, but as a strategic and multi-layered design approach. It highlights how minimalism can support project efficiency, enhance user satisfaction, and maintain cultural continuity throughout the entire life cycle of housing projects. In this context, the “M1-M13 Minimalist Housing Design Criteria” have been developed to extend beyond the formal dimensions of minimalism and enable the evaluation of design processes within a measurable, comparable, and data-driven framework. For example, M2 relates to user well-being, M5 to energy efficiency, and M10 to modular construction. The architectural narratives of seven housing projects from the U.S., U.K., Canada, India, and Portugal were analyzed using Natural Language Processing (NLP) techniques. Natural Language Processing (NLP) methods were used to analyze architectural texts, helping identify how frequently and deeply specific design themes, such as flexibility or sustainability, are emphasized in project descriptions. The study utilized Natural Language Processing (NLP) techniques, including Bag of Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), and conceptual keyword matching, to analyze a text corpus of approximately 1500-2000 words per project. These techniques identified how often and how deeply specific design themes appeared in the texts. Thematic densities were then mapped to the M1-M13 criteria using predefined keyword clusters, and each project was scored on a 0-6 scale for comparative visualization. The findings indicate that UDAAN and Platforms for Life exhibit high representation particularly in criteria such as modular construction, functional flexibility, and technical simplicity. In contrast, Adro and Park Hill demonstrate strong cultural contextuality, but limited technical efficiency. Moreover, several projects showed low levels of representation in socially sustainable criteria such as transparency in design, ease of intervention, and cultural continuity. Theoretical contributions of this study argue that minimalism cannot be defined solely by the principle of “less” but must be reconsidered as the expression of simplified, multi-dimensional decision-making strategies. At the methodological level, the study utilizes the analytical potential of natural language processing techniques, rarely employed in architectural research, to evaluate architectural narratives through a data-based approach, thereby enhancing the objectivity of architectural critique. Practically, the M1-M13 criteria serve as an applicable, modular, and replicable decision support tool for architectural education, design competitions, public housing policies, and sustainable urbanization strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".