Performance Analysis of Government and Public Buildings via Post Occupancy Evaluation
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
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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