A framework for evaluating the impact of buildings on inhabitant well-being
Bibliographic record
Abstract
• Well-being is a multifaceted concept with definitions that vary across disciplines. • Inhabitant well-being emerges from interactions between individual, collective and environmental domains. • A mixed-methods approach facilitates the analysis of relationships among elements. • Key Theoretical Constructs: relational dynamics, net positive outcomes, and collective approaches. Well-being in the built environment has become a significant area of interest for researchers and professionals across various disciplines. The evaluation of well-being is a significant challenge because the experience of inhabitants within buildings extends beyond their physical realm: it is shaped by interactions among individuals, building features, and relational dynamics within the environment and the broader community context. This multidimensional nature of well-being requires an interdisciplinary approach to deepen our understanding of how buildings impact inhabitant well-being. This study proposes a new framework to holistically evaluate the impact of buildings on inhabitant well-being by integrating collective dimensions of well-being and net-positive outcomes as well as focusing on the dynamic relationships among the components of the system. This marks a shift from a sum-of-the-parts perspective to a more holistic approach to building performance, where well-being emerges from the interactions between environmental, individual, and collective domains. The framework is grounded in a social practice perspective, employing mixed-method assessments that combine quantitative and qualitative methods. By operationalizing the framework, this study provides a roadmap for piloting assessment methods and analyzing multifaceted results, with the aim of uncovering collective and context-specific factors that influence inhabitant well-being. This approach seeks to bridge the gap between what is measured and what is experienced in the built environment, illuminating what truly matters to people and enhancing the relevance of design, operations, and management practices. It seeks to deepen our understanding of how these experiences can be better aligned with inhabitant needs and priorities, fostering interdisciplinary collaboration.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".