Evaluating mechanisms influencing residential satisfaction in federal housing estates in Abia state, Nigeria
Bibliographic record
Abstract
Residential satisfaction in public housing estates, its impact can better understand through buildings delivery processes and decision on resident’s response to initially and over the life cycle of the building. This study focused on mechanisms influencing residential satisfaction in federal housing estates in Abia State, Nigeria. An investigative post occupancy evaluation (POE) was the approach adopted for this study with questionnaire and interview conducted on the residents. A sample of 75 households were selected from 105 housing units within the two public housing estates. The sample represents 78.6% of the total public housing population. Data collected were analyzed using percentage, mean, and relative importance index (RII) on a 5 point likert scale questions with scaling namely: strongly disagree = 1, disagreed = 2, undecided = 3, agree = 4, and strongly agree = 5, in order to weigh the influencing mechanisms. It was found that more than half of the respondents (52.8%) agree that physical and social/environment characteristics are the components of residential satisfaction in public housing estates. Also, with RII of >0.60 the study established that the physical mechanism of residential satisfaction is buildings aesthetics, space, enclosure, location and health (personal/environmental). In the case of social mechanisms, the influencer of residential satisfaction is attitude to maintenance of facilities, educational and economic status of residents, safety and security, social needs and community involvement. Other residential satisfaction mechanisms identified in the study include maintenance of residential block, population density of the estate, and maintenance of public areas. Therefore, feedback mechanisms should be adopted by public estate developers; as data collected and information on buildings are better target to appropriate decision making, and the lesson learned are applied in the next building cycle of a similar facility type
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".