Emerging Issues in Economic Development: A Contemporary Theoretical Perspective: Essays in Honour of Dipankar Dasgupta & Amitava Bose
Bibliographic record
Abstract
Developing counties all around the globe have been trying to adopt market-driven institutional systems. As the pace of economic reforms gains momentum, it becomes increasingly evident that these reforms have resulted in new challenges and issues. For example, the economic growth which picked up pace in the last decade is now threatened by an unprecedented surge in inflation. Similarly the challenge to manage scarce foreign capital has now transformed into a challenge of maintaining policy independence under plentiful inflow of foreign exchange. Microfinance which was considered as a useful policy tool to eradicate poverty is now under closer scrutiny for pulling borrowers into debt trap. Though these challenges are theoretically similar to the ones in the past, they have undergone transformation due to recent developments in the field. This book analyses such emerging issues and attempts to bridge the gap between the empirical concerns and the existing theoretical understanding. The volume uses various frameworks ranging from moral hazard, adverse selection, influence peddling, trade theory, property rights among others to discuss issues such as corruption, financial scams, poverty measurement, voting behaviour, informal credit markets, technology transfer, and farmers' suicides. Contributors to this volume - Asis Kumar Banerjee, Institute of Development Studies Kolkata, India; Hamid Beladi, University of Texas at San Antonio, USA; Kalyan Chatterjee, The Pennsylvania State University, USA; Sarbajit Chaudhuri, University of Calcutta, India; Prabal Roy Chowdhury, Indian Statistical Institute, India; Romar Correa, University of Mumbai, India; Krishnendu Ghosh Dastidar, Jawaharlal Nehru University, India; Arijita Dutta, University of Calcutta, India; Bhaskar Dutta, University of Warwick, UK; Tarun Kabiraj, Indian Statistical Institute, India; Mukul Majumdar, Cornell University, USA; Indrajit Mallick, Centre for Studies in Social Sciences Calcutta, India; Sugata Marjit, Centre for Studies in Social Sciences Calcutta, India; Tapan Mitra, Cornell University, USA; Vivekananda Mukherjee, Jadavpur University, India; Anjan Mukherji, National Institute of Public Finance and Policy, New Delhi; Gordon Myers, Simon Fraser University, Canada; Meenakshi Rajeev, Institute for Social and Economic Change, India; Debraj Ray, New York University, USA; Dirk T.G. Rubbelke, Basque Centre for Climate Change (BC3), Spain, and IKERBASQUE, Basque Foundation for Science, Spain; Tilak Sanyal, Shibpur Dinobundhoo Institution, India; Abhirup Sarkar, Indian Statistical Institute, India; Abhijit Sengupta, University of Sydney, Australia; Seung Han Yoo, Korea University, Republic of Korea.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".