Contributions to the Field of Library and Information Sciences in Pakistan: A Bio Bibliometric Study of Dr. Saeed Ullah Jan
Bibliographic record
Abstract
Abstract Purpose- This paper presents the bio-bibliometric analysis of Dr. Saeed Ullah Jan's contributions to the Library and Information Science (LIS) field in Pakistan. This study includes the following: the year-wise distribution of research produced; authors' collaboration; publications by type; language; geographical preference for research; and coverage of different subject areas. Methodology/Design- The data for this retrospective study was requested through email from Dr. Saeed Ullah Jan with advance ethical permission and further verified from Scholar Google, the official university website, and departments where required. Findings- The results of the study indicate that Dr. Saeed Ullah Jan is a prolific writer and supervisor in LIS in Pakistan. He contributed 178 items, including 76 articles, two books, 86 theses, and fourteen conference papers, and secured eleven research grants until June 30, 2022. His most significant contribution is establishing two LIS departments with two postgraduate LIS education (MPhil and PhD) programs for the first time in Khyber Pakhtunkhwa. He has the honor of launching the first LIS Higher Education Commission (HEC) recognized research journal from the fertile land of Khyber Pakhtunkhwa, Pakistan. Research work by Dr. Saeed Ullah Jan has received worldwide recognition and has been accepted in leading journals in the United Kingdom, Canada, Japan, and the United States of America. He used the English language to publish the majority of his research work. He believes in teamwork, and about 98% of his research work was done in collaboration. He is also an HEC recognized and approved Ph.D. supervisor. Originality- This study is a unique biobibliometric study that systematically combined the research productivity of Dr. Saeed Ullah Jan and provided a holistic sketch of the literature produced on various themes of LIS during 2011–2022. Research limitations- Due to the limited area of biobibliometrics of a single author, the results were not generalized.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.034 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
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".