The Role of Human Capital Factors on Poverty in Informal Settlement: Informal Settlement of Sheikh-Hasan, Mashhad City, Iran
Bibliographic record
Abstract
This paper investigates the determinants of multi-dimension poverty in informal settlements of Mashhad City. It specially analyzes human capital factors, among factors that influence poverty level. Education, skills, experience and knowledge have important role in promoting income level and in access to sustainable jobs, especially in informal settlements that have lower human capital level than the urban areas other. Mashhad city has most marginal settlements in Iran. Sheikh-Hasan Neighborhood in Mashhad Municipality region 4 has been selected as case study. This study is based on information gathered from household level in 2016 and the ordered logit model is employed to estimate factors influencing urban poverty. Data were obtained from 300 households using the questionnaire Through the Systematic Random technique. Calculation of poverty indexes reveals that nearly 87% of households are below absolute poverty line and 20% of households are below extreme poverty line. Marginal effects show variables of “job stability”, “Ownership”, “Household size” and “Education of household head” have the greatest impact on poverty alleviation. Also, variables of “Education level” and “highest level of education of household members” have positive effect and significant on poverty. Results represent that poverty in informal settlements of Mashhad is strongly linked to factors such as human capital. In addition, with increasing the level of knowledge of household heads and creation of favorable conditions for increasing of the education level of household members can reduce poverty.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".