Analysis of the Low-Income Housing in Isfahan Metropolis
Bibliographic record
Abstract
Despite the importance of housing in human life, the provision of adequate and affordable housing for all people is one of the current problems of the human society because almost half of the world’s population lives in poverty and about 600 to 800 million people reside in substandard housing conditions. The present study, therefore, has been conducted in order to identify the needy groups and, too, housing the low-income groups in Isfahan City. The study is a fundamental-applied research adopting a descriptive-analytical methodology. Variables of the research are the income deciles, housing quantity developments, land and housing prices, the system of housing finance, housing status in the expenditure basket of the low-income households, the Gini coefficient of housing costs, the effective demand for housing in the income deciles considering the area of infrastructure and the access to housing index. The findings reveal that the year 2008 had the highest increase in the housing prices with an increase as 20.4% and the lowest one refers to the year 2010 with an increase as 8.6%. The Gini coefficient of housing cost for urban households shows a downtrend until 2005 and from 2006 onward, the gap has started to increase. Regarding access to the housing index, the results show that in 2003 the low income decile could afford one square meter of housing by saving the total household income for 75 days; whereas in 2011, this degree raised to 206 days. What is noteworthy here is the deep gap between the high-income and low-income deciles in the saving days for one square meter, which differs 10.5 times between the first decile and the tenth decile.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".