The Determinants of Poverty in Informal Settlement Areas of Mashhad (Case Study: Shahid Ghorbani Quarter)
Bibliographic record
Abstract
Urban poverty has long been a concern of urban and development debates, and has been an important focus in social science research. Informal settlement in Mashhad city is highlighted because of its wide spreading and severity. This study aimed to determine the causes of urban poverty in informal settlement regions. The data were collected from household level questionnaire in 2016 and the Logistic Regression Model was performed to identify the determinants of urban poverty. The data were obtained from 220 households who settled in Shahid Ghorbani quarter using the questionnaire through the Systematic Random technique. Nearly 87 of households of the studied area were below absolute poverty line and 20 of them were below extreme poverty line. Given that all household heads in the sample were married men, significant relationships were observed between poverty and characteristics like “age of household head”, “being self-employed”, “household size”, “the ratio of worker in household”, “ownership of house” and “having social security”, while factors like “Access to services and infrastructures” and “education” had no significant impact on the likelihood of moving out of poverty. The results also revealed that if the household head is older and self–employed, the likelihood of being poor is gradually diminished. Also if the family members had some kind of social security or owned their houses, household welfare would improve; however, increasing in household size and ratio of worker in household would decrease household welfare. Eventually, the marginal effects of variables were interpreted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".