Bibliographic record
Abstract
Suburbanization or as it is called today, informal settlement, is the consequence of rapid urbanization and transfer to socioeconomic paradigm of industrialization.Swift and unbalanced physical development of cities has been followed by undesirable economic, social and skeletal consequences.One of the effects and consequences of inharmonious urban physical development includes suburbanization and informal settlement.Nowadays, thousand million people of the world especially in developing countries live in unauthorized and disturb residences and under poor economic, social and environmental conditions.According to the report released by the UN, informal settlements (suburbanization) have been identified as the main challenge of the third millennium.On the whole, in the world one out of every six individuals lives in informal settlements.About 2 billion persons of the urban population of the world will live in such residences until the year 2030.In this research, first, the concept of informal settlement and different theories about the subject of research has been studied.Later, the corresponding problems with informal settlements and characteristics of these residences will be explained in detail.At the end, the approaches for intervention with informal settlements are studied from the beginning through the present time.The main goal of this research is to describe the respective problems with this type of residences and recognition of the intervention approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.970 | 0.967 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".