Identifying Contextual Socio-Cultural Attributes as Predictors of User Satisfaction: A Study in Transformed Public Housings in Nigeria
Bibliographic record
Abstract
Transformation of houses is common, especially when households inhabit them for a considerable period of time. However, the scale of transformation might vary. Public housing in Nigeria has seen large scale transformations, and started to generate wide attention. The transformation phenomenon is often attributed to the exclusion of socio-cultural values in initial design that eventually results in unguided densification. But at the same time, house transformations arise from people's desires to satisfy their own ever changing housing needs. Using transformation as a sign of dissatisfaction, this study was an attempt to identify local socio-cultural attributes lack of which result in such transformations. Conditional sampling was adopted in order to find respondents across Nigeria. Socio-cultural attributes were identified from background study and were used in structuring a questionnaire-based survey. Findings from univariate and psychometric analysis based on the survey indicated that social activities and family structure were the two most significant socio-cultural attributes that guide residents’ transformation decisions in public housing adjustments. This finding also appeared to be crucial as initial layouts proved not to lead to users’ satisfaction. These results might be useful for prospective developers who are explicitly seeking a successful and sustainable delivery system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".