Rethinking Performance Gaps: A Regenerative Sustainability Approach to Built Environment Performance Assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Globally, there are significant challenges to meeting built environment performance targets. The gaps found between the predicted performance of new or retrofit buildings and their actual performance impede an understanding of how to achieve these targets. This paper points to the importance of reliable and informative building performance assessments. We argue that if we are to make progress in achieving our climate goals, we need to reframe built environment performance with a shift to net positive goals, while recognising the equal importance of human and environmental outcomes. This paper presents a simple conceptual framework for built environment performance assessment and identifies three performance gaps: (i) Prediction Gap (e.g., modelled and measured energy, water consumption); (ii) Expectations Gap (e.g., occupant expectations in pre- and post-occupancy evaluations); and, (iii) Outcomes Gap (e.g., thermal comfort measurements and survey results). We question which of measured or experienced performance is the ‘true’ performance of the built environment. We further identify a “Prediction Paradox”, indicating that it may not be possible to achieve more accurate predictions of building performance at the early design stage. Instead, we propose that Performance Gaps be seen as creative resources, used to improve the resilience of design strategies through continuous monitoring.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it