Investigation of Ethnic Diversity In Pakistan: A Case Study of Karachi
Bibliographic record
Abstract
In developing countries increasing population and lack of good governance are the major issues and Pakistan is no exception. These issues are highlighted most in the context of urban centers. Karachi is the most populous city of Pakistan and currently ranked 6th among the mega cities of the world. After partition this city has grown up rapidly due to influx of both national and international immigrants. Urdu speaking persons are the largest ethnic group in Karachi and Pashtuns are second largest group who came to Karachi. It can’t be wrong to say that there are even more Pashtuns in Karachi than in Peshawar itself. Almost 50% of the total population is Urdu speaking whereas 25% of total population is Pashtun in Karachi, whereas, 14% are from Punjab and remaining 9% speaking other languages are settled here. Similarly there are areas in the city marked for Christians, Hindus, Parsis etc. Census Data for the years 1951, 1961, 1972, 1981 and 1998 are taken to analyze the variability of Religion, Population and Language (language data is not available for 1972 census). Spatial development of Slums / Katchi-Abadi 1988 and projection for 2000 has also been discussed. The change in population for the years 1981 and 1988 has been mapped using GIS. MP & ECD Analysis Zones have been used and according to the findings during the years 1986-2000 population increase percent is higher in Surjani Town and Taisar Town. It is also concluded that 96% population living in the city are Muslims and speak more than 9 different languages.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".