Ten questions concerning well-being in the built environment
Bibliographic record
Abstract
Well-being in the built environment is a topic that features frequently in building standards and certification schemes, in scholarly articles and in the general press. However, despite this surge in attention, there are still many questions on how to effectively design, measure, and nurture well-being in the built environment. Bringing together experts from academia and the building industry, this paper aims to demonstrate that the promotion of well-being requires a departure from conventional agendas. The ten questions and answers have been arranged to offer a range of perspectives on the principles and strategies that can better sustain the consideration of well-being in the design and operation of the built environment. Placing a specific focus on some of the key physical factors (e.g., light, temperature, sound, and air quality) of indoor environmental quality (IEQ) that strongly influence occupant perception of built spaces, attention is also given to the value of multi-sensory variability, to how to monitor and communicate well-being outcomes in support of organizational and operational strategies, and to future research needs and their translation into building practice and standards. Seen as a whole, a new framework emerges, accentuating the integration of diverse new competencies required to support the design and operation of built environments that respond to the multifaceted physical, physiological, and psychological needs of their occupants.
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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".