Performance Analysis of Government and Public Buildings via Post Occupancy Evaluation
Bibliographic record
Abstract
The government has an important obligation to ensure that the public buildings and facilities should be well managed to maintain building sustainability. Evaluation after occupancy in buildings is vitally needed to ensure that building performance is sustained. Post Occupancy Evaluation (POE) of buildings is of utmost importance in building performance evaluation as it comprises the technique that is used to evaluate whether a building meets the user’s requirement. By using occupants as benchmark in evaluation, the potential of improving the performance of building is enormous. This paper discusses about a research with the broad aim of developing a general guideline for the POE practice specifically for government and public buildings in Malaysia. The entailing objectives are firstly, to review and analyze the government and public building performance, secondly, to determine the occupants’ satisfaction level, and thirdly, to determine the correlation between building performance and occupants’ satisfaction level. The study has revealed that 74% of the aspects of building performance are in high correlation with the occupants’ satisfaction. The study concludes that the proposed guideline of POE is effective, relevant and beneficial to be used by public sector in evaluating the performance of government and public buildings in Malaysia.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".