Green Building Perception Matrix, A Theoretical Framework
Bibliographic record
Abstract
Research has consistently shown that architects differ from the public in what they prefer in buildings. Today, as building design and construction evolve to more sustainability, some recent studies show that the overall level of satisfaction of occupants of green buildings still does not exceed the level of satisfaction in conventional structures. Satisfaction is typically measured, with Post Occupancy Evaluation, which gathers feedback from building occupants about aspects such as comfort, indoor air quality, and aesthetics. This raises some questions: Do people perceive green building design as consistent with their desire for sustainability? Do ratings of green buildings by systems such as LEED or BREAM affect the level of satisfaction of laypeople? Can owners and occupants of green buildings be considered as green consumers, who are attracted to green products because of their willingness to mitigate the impact of human activities on the environment? This article examines Peattie’s (2001) green purchase perception matrix as a means of understanding occupants’ perceptions of green-labeled buildings. An analytical approach has been taken to identify the influential factors, which are involved in this relationship. As a result, the authors propose a green building perception matrix that addresses the compromise that occupants must make in green buildings and the confidence that building systems are indeed making a difference environmentally. Understanding and using this matrix may help green building designers to improve the level of satisfaction of building’s owners and occupants. The discussion is critical for future research on how green building design attributes can be used as a catalyst for green consumption behavior.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".