Rapid Urbanization as a Source of Social and Ecological Decay: A Case of Multan City, Pakistan
Bibliographic record
Abstract
This paper concentrates on the relationship between rapid urbanization and socio-ecological problems. The major objective of this study is to analyze the unplanned and haphazard urbanization that is giving birth to environmental issues such as; pollution, poor drainage system, poor quality of drinking water and poor hygienic conditions. This research carried out in Multan city, Pakistan through field survey of 200 respondents using multistage sampling technique. Self-administrated questionnaire was used as a tool of data collection and the binary logistic regression was employed for the analysis of the data. The results depict that urbanization is one of the major causes of converging joint family system to the nuclear family system and its changing function as a consequence. It is also a source of reduction of greenery and trees in the city. It is causing problem of poor sanitation system and quality of drinking water. Pollution is another outcome of haphazard and unplanned urbanization. The researcher also found that due to migration from rural to urban areas, the life in the city implicates adversely the quality of life. This study provides better insight on the problems of urbanization in urban areas and will also help policy makers to focus on major areas of improvement such as to check the migration from rural to urban. To enforce the urban laws to reduce the problems of sanitation, check on transport system, quality of drinking water, domestic and industrial waste. The researcher suggests the monitoring of the migration from rural to urban areas through provision of basic facilities in rural areas. On the other hand awareness campaigns and provision of basic facilities to the rural people (educational facilities, health facilities, food and empowerment in basic decision making) can reduce this problem.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".