Arguments for a Better World: Essays in Honor of Amartya Sen: Volume I: Ethics, Welfare, and Measurement
Bibliographic record
Abstract
Amartya Sen has made deep and lasting contributions to the academic disciplines of economics, philosophy, and the social sciences more broadly. He has engaged in policy dialogue and public debate, advancing the cause of a human development focused policy agenda, and a tolerant and democratic polity. This argumentative Indian has made the case for the poorest of the poor, and for plurality in cultural perspective. It is not surprising that he has won the highest awards, ranging from the Nobel Prize in Economics to the Bharat Ratna, India's highest civilian honor. This public recognition has gone hand in hand with the affection and admiration that Amartya's friends and students hold for him. This volume of essays, written in honor of his 75th birthday by his students and peers, covers the range of contributions that Sen has made to knowledge. They are written by some of the world's leading economists, philosophers and social scientists, and address topics such as ethics, welfare economics, poverty, gender, human development, society and politics. This first volume covers the topics of Ethics, Normative Economics and Welfare; Agency, Aggregation and Social Choice; Poverty, Capabilities and Measurement; and Identity, Collective Action and Public Economics. It is a fitting tribute to Sen's own contributions to the discourse on Ethics, Welfare and Measurement. Contributors include: Sabina Alkire, Paul Anand, Sudhir Anand, Kwame Anthony Appiah, A. B. Atkinson, Walter Bossert, Francois Bourguignon, John Broome, Satya R. Chakravarty, Rajat Deb, Bhaskar Dutta, James E. Foster, Wulf Gaertner, Indranil K. Ghosh, Peter Hammond, Christopher Handy, Christopher Harris, Satish K. Jain, Isaac Levi, Oliver Linton, S. R. Osmani, Prasanta K. Pattanaik, Edmund S. Phelps, Mozaffar Qizilbash, Martin Ravallion, Kevin Roberts, Ingrid Robeyns, Maurice Salles, Cristina Santos, T. M. Scanlon, Arjun Sengupta, Tae Kun Seo, Anthony Shorrocks , Ron Smith, Joseph E. Stiglitz, S. Subramanian, Kotaro Suzumura, Alain Trannoy, Guanghua Wan, John A. Weymark, and Yongsheng Xu. Contributors to this volume - Sabina Alkire, University of Oxford Paul Anand, Open University Sudhir Anand, University of Oxford Kwame Anthony Appiah, Princeton University A. B. Atkinson, University of Oxford Walter Bossert, Universite de Montreal Francois Bourguignon, Paris School of Economics John Broome, University of Oxford Satya R. Chakravarty, Indian Statistical Institute Rajat Deb, Southern Methodist University Bhaskar Dutta, The University of Warwick James E. Foster, Vanderbilt University Wulf Gaertner, University of Osnabruck Indranil K. Ghosh, Winston Salem State University Peter Hammond, The University of Warwick Christopher Handy, Cornell University Christopher Harris, University of Cambridge Satish K. Jain, Jawaharlal Nehru University Isaac Levi, Columbia University Oliver Linton, London School of Economics and Political Science Siddiqur R. Osmani, University of Ulster Prasanta K. Pattanaik, University of California, Riverside Edmund S. Phelps, Columbia University Mozaffar Qizilbash, University of York Martin Ravallion, World Bank Kevin Roberts, University of Oxford Ingrid Robeyns, Radboud University Nijmegen Maurice Salles, Universite de Caen Cristina Santos, University College London Thomas. M. Scanlon, Harvard University Arjun Sengupta, Indian Parliament Tae Kun Seo, Southern Methodist University Anthony Shorrocks, UNU-WIDER Ronald Smith, Birkbeck College Joseph E. Stiglitz, Columbia University S. Subramanian, Madras Institute of Development Studies Kotaro Suzumura, Hitotsubashi University Alain Trannoy, L'Ecole des hautes etudes en sciences sociales Guanghua Wan, UNU-WIDER John A. Weymark, Vanderbilt University Yongsheng Xu, Georgetown State University
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".