The POE paradigm in architecture: practices and perspectives of Canadian practitioners
Bibliographic record
Abstract
The benefits of and barriers to post-occupancy evaluation (‘POE’) in industry are regularly described in the literature as relating to building performance and sustainable design. However, the perspectives of architects on the value of POE to design practices or processes are rarely included. The purpose of this grounded theory study was to address this gap, contributing Canadian practitioners’ perspectives to stagnate conversations on the lack of architects’ engagement with POE. Semi-structured interviews were conducted with 25 practitioners from 20 different architecture firms across the country as well as 5 POE specialists working in academia and/or industry. The main finding of this study indicates that Canadian practitioners see theoretical value in POE; however, they are sceptical about its practical application and value to their design work in its present state. This has engendered disciplinary paralysis when it comes to embracing this research pathway and establishing their role in it. Further analysis indicates a need to improve the value proposition of POE in the industry with implications for researchers, architects, policymakers and professional organizations. This includes rebranding and rescoping POE, cultivating methodological richness, refocusing on the user experience, building safeguard mechanisms and building tolerance through precedent.
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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 | 0.031 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.033 | 0.031 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".