Mass Customization of Housing: A Framework for Harmonizing Individual Needs with Factory Produced Housing
Bibliographic record
Abstract
Integrated processes for design and fabrication have guided mass customization of architectural systems and components. Providing affordable and accessible housing, a vital segment of the building industry, is a multifaceted process that witnessed various manifestations towards individualization over the past few decades. Design flexibility in housing systems is becoming a crucial aspect, informed by consumers’ lifestyles, demographic patterns, and lifecycles change at a rapid pace. As the housing market demands more personalized, efficient, and agile strategies, prefabricated building systems have always presented a viable alternative for flexibility and customization, following a rise of interest in the last decade focused on new modes of digitized design and production. This paper presents an overview and appraisal of various practices to implement customization in the housing industry, with specific focus on empowering a systemic approach. We then propose an open framework that could accommodate emergent design technologies and production protocols, with the aim of taking advantage of advanced research efforts, and coupled with current industry application.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".