Bibliographic record
Abstract
The objective of this paper is to identify and analyze the principles, approaches, and strategies involved in the design of residential buildings that explicitly take into account changing needs over a given building’s life. In the view of the researchers, this pursuit is of the utmost significance, particularly in the last few decades—which can be characterized, socially and physically, by rapid shifts. For many industry professionals, flexible design has been branded as costly, difficult to deploy, and demanding state-of-the-art gadgetry. Therefore, after more than a century of attempts to design for flexibility, the issue is arguably still marginalized to the profession at large. Through synthesizing the existing literature, it became clear that design approaches have focused primarily on physical flexibility (i.e., capacity to change the spatial structure). This overly narrow approach leaves the user and the environment out of the equation, leading to the inevitable failure of the built environment's capacity to respond to social or environmental changes.Admittedly, the attention on low operational and embodied carbon of buildings is greatly supported by near and long-term legislation agendas, particularly in the developed world. However, the present paper is after a measure that is more independent, responsive and holistic; a measure that integrates aspects of durability, flexibility and responsibility; that introduces all layers of physical, social, environmental and economic factors in the form of continuously evolving and dynamic framework; a measure that we refer to as Agile. Yet, a standard theoretical framework for setting such Agile concepts is not yet established. The proposed Agility framework consists of two parts, 1) Design Toolkit and 2) Mechanisms, Plans, and Procedures to inform Policy. The design toolkit is a three-step process, namely, 1) identify strategy clusters, 2) analyze user needs and strategies’ objectives, and 3) evaluate the ‘value’ of the proposed strategies. The goal is to advocate a scientific approach to channel human creativity into its most productive form, eventually improving our judgement by subjecting our theories to repeated testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.021 | 0.028 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".