Bibliographic record
Abstract
Dr. Prem Bhandari is a distinguished social scientist and a member of the International Advisory Board of Dhaulagiri Journal of Sociology and Anthropology (DJSA). Dr. Bhandari was born and raised in rural Nepal, where he began his academic pursuits. His educational journey continued across Kathmandu, Chitwan, Thailand, and the USA, covering diverse fields of study including biology, agricultural economics, rural development planning, and rural sociology and demography. Dr. Bhandari completed his PhD in Rural Sociology and Demography from the Pennsylvania State University, USA. His research interests are broad, spanning fertility, migration, rural livelihoods, population health, and the socio-economic and cultural determinants of social change, with a particular focus on Nepal and South Asia. His current research focuses on factors contributing to profitability in agritourism, visitor attractions, and activity diversification in agritourism. His academic career spans several prestigious institutions, including Tribhuvan University, Nepal; the University of Alberta, Canada; the University of Michigan; and the University of Maryland Eastern Shore. In Nepal, Dr. Bhandari has contributed to applied research in social science through the Institute for Social and Environmental Research-Nepal (ISER-N). In addition to his roles in teaching, research, and Extension, he has been deeply engaged in national and international research collaborations, hosting and participating in conferences, mentoring scholars in Nepal and the U.S., and serving as an editor for academic journals. His positive attitude, warm heart, simplicity, and willingness to share his valuable time, experiences, and skills, and his contribution through publishing research articles in food security, migration, and research methods for the DJSA have encouraged us to get to know him better. We believe his life trajectory will serve as a profound source of inspiration for emerging scholars to shape their own lives. We are thankful and extend our sincere gratitude to Dr. Bhandari for accepting the proposal for a written interview, in which he has generously and thoughtfully engaged.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.016 |
| Insufficient payload (model declined to judge) | 0.043 | 0.018 |
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".