Bibliographic record
Abstract
Pakistan has been traditional and modern education systems since its inception. The traditional education system or traditional Islamic schools which are called Madaris (Madrassahs) have been growing rapidly since 1980s during the Soviet intervention into Afghanistan and Imam Khumeni Revolution in Iran. The madaris have been influenced by the wave of Islamic fundamentalism from Iran and Afghanistan. After the Taliban Government in Afghanistan in late 1990s, the Deoband Madaris in Pakistan came gradually under the sway of the anti-West Taliban Movement. In 2000, there are more than 50,000 Madrassahs in Pakistan but only 4,350 were registered. The history of Madrassahs education in Pakistan has been originated by the advent of Islam and Arabic culture to India with conquering of Sindh by Muhammad bin Qasim in 712 A.D. After the formation of Muslim rule at Delhi in 1208 A.D., a quarter of Indian-subcontinent population had converted to Islam over the next five centuries and Madrassahs were established in India. Those madaris had been providing education among Muslims of India from 1208 A. D. to 1757 A. D. till the British power in India. The Madrassahs in Pakistan are alleged to be nurseries to produce religious extremists. The purpose of this paper is to highlight the history and the changing pattern of Madrassahs Education in Pakistan. The paper is also examining the socio-political framework of Pakistan under which the Madrassahs are working.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".