RESEARCH WORK ON URDU IN UNIVERSITIES
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Academic Research on Urdu Language and Literature started much before creation of Pakistan, but there were very few who excelled to the level of PhD. Although there was no shortage of topics but researchers, academicians and universities offering PhDs were rare. Even Pakistan’s oldest University of Punjab established department of Urdu in 1948, Karachi University followed couple of years later. For the first 20-25 years of Pakistan not much could be achieved in field of credible research on Urdu. \n \nIt only improved in 1973 when Dr. Waheed Qureshi took charge of Urdu department, Punjab University. He opened new doors for research relating activities, engaged and supported researchers in there work, which gave tremendous boost to academic research on Urdu. His predecessors Dr. Ghulam Hussain, Dr. Khwaja Muhammad Zakria and Dr. Sohail Ahmad Khan had the same generous attitude towards research. In my tenure as head of Urdu Department (2000-2001), regular PhD program was initiated in parallel to already existed Private PhD program, greatly increasing number of PhD students. At the same time Allama Iqbal Open University, Universities of Karachi, Sindh, Multan and Bhawalpur had there own M.Phil and PhD programs. Research on Urdu Language and Literature is not confined to universities in Pakistan. Research work on Urdu is carried out globally. Universities in USA, Canada, UK, France, Belgium, Turkey, Bangladesh and specially India are awarding M. Phil and PhD degrees in Urdu. \n \nNeed for Comprehensive List of Topics \n \nThough the advancement and expansion on research on Urdu is encouraging but there are also some points to ponder about such as lack of coordination between Urdu departments of universities. No university has any idea of topics others are working on or have been researched, nor there is any elaborated and comprehensive list of topics available one can consult before assigning a topic to researchers resulting in repetition of topics. One can find multiple research papers on one topic, of which many might be from same university. This is sheer wastage of time, resources and expertise. \n \nAnother disadvantage of non availability of a comprehensive list is that research trends can not be judged, which hinders search for new topics. \n \nMy association with academic research is spread over 40 years and I am affiliated with M.A, M. Phil, and PhD level research work being done in Punjab and Allama Iqbal Open universities. It was always difficult to confirm if any previous work is available on a topic, at times after putting months of work on a topic one gets the knowledge that some university had already researched on the topic or has assigned the topic for research. \n \nDue to above mentioned reasons, need for a comprehensive list of topics is evident. I over the years kept notes on credible research work done, using my notes and other available documents complied this list.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it