Bibliographic record
Abstract
Born on March 7, 1952, into an educated and socially active family, Professor Samira Luitel is a distinguished sociologist and education consultant based in Kathmandu, Nepal. With a professional career spanning over four decades, she has made substantial contributions to the fields of education, gender studies, and social inclusion. Since her retirement in 2013, she has continued her work as a freelance consultant. Professor Luitel has held significant academic and research positions, including Senior Researcher at the Research Centre for Educational Innovation and Development (CERID) and Professor at the Central Department of Sociology/Anthropology at Tribhuvan University. She has also served as a Supplemental Professor at the University of Delaware, USA. Her research focuses on the intersection of education and development, particularly concerning women, ethnic minorities, and other disadvantaged groups, employing both qualitative and quantitative methodologies. She holds a Ph.D. in International Intercultural Education from the University of Alberta, Canada. Her dissertation explored literacy and empowerment among Maithili women in Nepal. Professor Luitel has authored several publications, including books, manuals, and over two dozen research reports on gender, education, and social issues. As the first female professor of Sociology in Nepal and an active member of numerous academic and development organizations, she has significantly influenced policy-making, curriculum development, and capacity-building initiatives. Her contributions have been recognized with the Mahendra Bidhya Bhushan award for academic excellence. Professor Luitel continues to impact Nepal’s education and social development sectors through research, teaching, and advocacy. This interview was conducted in March 2025 by Professor Madhusudan Subedi and Dr. Man Bahadur Khatri, and the excerpts are presented in the order in which the questions were asked.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.034 | 0.014 |
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".