Bibliographic record
Abstract
Education plays a pivotal role in the economic and social development of any country. Only a balance Education sytem can develop a healthy society. After 1857, Muslims emerged in India with two parallel but different directions of education system. Dãrul Üloom Deoband and Aligarh education system. This difference divided education into religion (Deen) and the world (Duniya).According to Pakistan's 2017-18 educational statistics, out of 3,05,763 educational institutions in the country, there are 62% government, 28% private, and 10% religious madrassas. These institutions differ from one another in terms of their curriculum, environment, medium of instruction and examination system. As a result of these different types of education systems, young people, having different mind sets and conflicting philosophies of life are emerging in the society. Earliest ending of this class difference in Pakistan is a big challenge. If we look at the glorious past of Islam, there was the knowledge of religion and the world at the same time and no difference among rich and poor for education. We can systematically achieve a uniform education system by introducing uniform curriculum, medium of instruction, teaching methods, environment and examination methods. The present government has taken the first step in this direction with a "Single National Curriculum" which can be expected to restore some degree of harmony in the society and pave way for the achievement of therest of the goals.
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 | 0.001 | 0.002 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.974 | 0.979 |
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".