An exploration of post-occupancy evaluation in Canada: origins, milestones and next steps
Bibliographic record
Abstract
Several histories by prominent researchers in the field locate Canada among the early adopters and entrepreneurs of post-occupancy evaluation (‘POE’). Despite its reputation for being in use in Canada, POE has lived comfortably on the margins of the Canadian building industry for decades, taking a backseat to more prescriptive and prognostic approaches to building performance and design quality. This paper provides a semi-systematic review and content analysis of POE as it appears today in Canadian research, policy, practice and education. Results are presented in two parts. The first part details the untold history of POE in Canada, its origins and milestone contributions, and current directions. The second part summarizes results from the literature search and discusses relevant findings. Findings show siloed efforts in POE continue to be the primary barrier to the mainstreaming of POE in Canada. Canada is, however, making headway in post-occupancy verification programmes and these programmes have the potential to pave the way for POE as an industry best-practice. The paper concludes with several recommendations to advance POE efforts, including establishing pan-Canadian performance indicators, building bridges between sectors and continuing to normalize data collection and sharing.
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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 | 0.046 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.027 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".