Migration and Informal Settlements as Spatial Expression of Social Inequality in Iran
Bibliographic record
Abstract
In this paper, on base that immigration motivations a result of social inequality cause the formation of informal settlements, by selecting a sample of informal settlements in the cities of Iran (Vali-e-Asr quarter in Qom) on this topic exploring been tried. Current research has exploratory and analytical nature. The data collection has been two types of library and field (questionnaire). In the field study that 150 households were selected with simple random sampling method, and the data collected from them was imported in the SPSS software, and meanwhile the classification and sorting, action has been to mining and exploration information. Results of this research indicate that informal settlements in Vali-e-Asr quarter in the city of Qom like many other informal settlements in Iran, the phenomenon of migration is twin so that 100 percent of the residents of this neighborhood's population are immigrants. Also all mechanisms of migration residents of the quarter, from its primary habitat and origin until the selection of Qom as an immigration destination, and living in Vali-e-Asr quarter represents the heavy shadow of social inequality among the people of Iran. In fact, spatial disparities (the unequal distribution of facilities and services at the national, regional and local) during the past decade, the main cause of migration and the formation of informal settlements in Iran, a fact that represents a major challenge to sustainable urban development in the country
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".