Contextual drivers of occupants’ comfort and behavior in buildings: A review
Bibliographic record
Abstract
Comfort is a multifaceted concept, varying across different fields and contexts. In building environments, occupant comfort results from a complex interplay of physical, personal, and contextual factors. While physical variables have been extensively studied, the impact of contextual variables, especially those tied to architectural and interior design, remains underexplored. This manuscript highlights the gaps in the literature by analyzing 127 papers, offering a detailed and critical examination of the current understanding in the field, and distinguishing contextual variables from other experimental variables in the literature. The review details the various comfort domains and examines prior studies’ experimental settings and methodologies. The findings indicate that while variables such as office layout have been extensively examined, other factors such as workstation location, floor level, materiality, and furniture have received minimal attention. Furthermore, most existing studies were conducted in operational buildings, where confounding variables precluded the isolation of specific contextual factors. The review also emphasizes the scarcity of studies employing more rigorous and controlled experimental setups. It also contributes to the knowledge of contextual variables in the literature by adding categories and variables that previous studies have not explored. A framework is proposed for future studies focusing on the visual perception of contextual variables within relevant building typologies. It is recommended that controlled environments, advanced simulation techniques, and objective measurement tools be used in future experiments to isolate and accurately evaluate the impact of contextual variables on occupant comfort. Addressing these deficiencies will help advance the field by developing design guidelines and standards that enhance the well-being and productivity of building 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".