Relation between Demographic Profiles and Residential Satisfaction Among Iranian Citizens: A Case Study
Bibliographic record
Abstract
Housing is a basic human need and a basic human right and one of the most important issues that humans have always been struggling with and trying to find a proper and reasonable answer to this problem. As such, one of the significant aspects in this context is the residential satisfaction from the residents’ perspective. Having this mind, residential satisfaction among the city dwellers in most urban areas become highly unclear and for Mashhad’s urban areas, there is dearth of knowledge. Therefore, the current study was an endeavor to explore the level of residential satisfaction among the residents in District 11 of Mashhad, Iran and to explore whether residential satisfaction rates differed according to demographic information including gender, job type, age, and residency history. A questionnaire on Residential Satisfaction examining four categories of body, facilities, utilities, and economy using 22 questions, which showed a reliability of 0.81 was completed by 70 participants. The Statistical analyses revealed that residential satisfaction level among the participants of this study was above average and the highest satisfaction rate belonged to the body of the apartment whereas the lowest category went for utilities. Moreover, the residents showed dissatisfaction with parking, followed by garbage collection, and the asphalt in the neighborhood as the biggest concerns; yet, they were highly satisfied with water, electricity, gas, telecommunication and drainage system. As for the demographic data of the residents, it was found that there was no statistically significant difference between males and females in terms of their satisfaction rate, and that no significant difference was found in terms of job type, i.e. governmental or private, while the same results was found concerning the age. Finally, there was no significant difference between residency history and residential satisfaction level.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".