The Indian Knowledge System Based Graduate Learning: A comprehensive course for University Students
Bibliographic record
Abstract
Preface: the strength of this IKS course for graduate diploma and degree students This One-Year Certificate and Two-Year Master's degree curriculum on IKS has been developed based on decades of experiences of learning, reflecting, researching, and doing fieldwork on the Indian Knowledge System as part of my research on Medical Humanities. The research culminated in writing two books, and several papers, and training four PhD students on IKS-related bio-medical research. While designing the curriculum, I emphasized the thoughtful blending of traditional wisdom and knowledge of ancient India, contemporary relevance, modern qualitative research methodologies, and interdisciplinary perspectives. Special attention has been given to providing students with a comprehensive understanding of India’s rich intellectual, medical, and economic developmental heritages, and the modern approach for the utilization of these heritages for global well-being and growth. Care has been taken to incorporate my understanding of the modern application of IKS in various domains of the globalization process including integrated health, sustainable living, and environmental ethics. Students are given opportunities to learn methodologies to expand their fieldwork and project development experiences through several innovative fieldwork and capstone projects that I have directed since 2018 as a part of the graduate development program; several of these project reports are published as listed at the end of this prospectus. These fieldwork courses are designed for students to actively engage in hands-on research and practical real-world experiences so that they can develop practical skills, and deepen their understanding of theoretical concepts of knowledge emergence in IKS as per my hypothesis in the 2003 paper on IKS presented in Mumbai. The fieldwork courses are integrated with classroom learning for students to be able to connect work experiences with theories, thus bridging the gap between academic knowledge and practical applications. Some of the fieldwork and thesis projects may lead to certification in a specially chosen field such as physician assistant in health and wellbeing as detailed at the end of this prospectus. Thus, the course is designed to prepare students for future careers or advanced research as well as prepare them for global competency.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.061 | 0.045 |
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".