Bibliographic record
Abstract
Extract Kumar V Pratap is passionate about infrastructure and public–private partnerships. Currently, he is Senior Economic Adviser in the Government of India. Earlier, he has worked with the Prime Minister’s Office (as Deputy Secretary) and Ministry of Finance (as Joint Secretary, Infrastructure Policy and Finance) at New Delhi, and the World Bank in Washington, DC.1 He has made seminal contributions in formulating the asset monetization policy of the Government of India as well as the ‘electronic auction of coal’ and the ‘competitive bidding of coal blocks’ policies. He also contributed extensively to the G20 Principles for Quality Infrastructure Investment adopted by the G20 leaders in 2019. He was Member Secretary of the task force that prepared India’s first National Infrastructure Pipeline. In the past, he was part of the task force for setting up a road regulator, and the Chair of the Committee writing the Model Concession Agreement for PPPs in the Urban Water Supply sector. He led the Indian delegation at the G20 Infrastructure Working Group meetings. He is currently on the Board of Directors of the North Eastern Development Finance Corporation (NEDFi) and was earlier on the Board of Directors of ONGC Videsh Limited, India Infrastructure Finance Company Limited (IIFCL), IRSDC, Indian Railway Finance Corporation, AIIB (Beijing), and New Development Bank (Shanghai). He has written a book, PPPs in Infrastructure: Managing the Challenges, published by Springer (Singapore) in 2018. He has also published with Oxford University Press, the World Bank, University of Melbourne, Economic and Political Weekly, SAGE journal Vikalpa, and the popular press including Economic Times, Business Standard, and Financial Express. He was a visiting professor at the Indian School of Business (Hyderabad and Mohali) from 2013 to 2017 teaching a strategy and policy elective on ‘Infrastructure and the Private Sector’. He has also lectured at the University of Michigan (Ann Arbor), London School of Economics, Singapore Management University, Lee Kuan Yew School of Public Policy (Singapore), Duke University, University of Maryland, World Bank (Washington, DC), IMF (SARTTAC), Indian Institute of Management (IIM, Ahmedabad), IIM (Lucknow), IIM (Indore), IIM (Shillong), National Academy of Administration (Mussoorie), and National Institute of Public Finance and Policy (Delhi). He is a recipient of University of Maryland’s John J. Sexton and doctoral fellowships, a letter of appreciation from the Indian Prime Minister, the National Talent Search Examination (NTSE) scholarship, the University of Melbourne’s Emerging Leaders Fellowship, and the Schulich Business School’s (Canada) Sustainable Infrastructure Fellowship.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.030 |
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; both teacher heads agree on what is shown here.
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".