Bibliographic record
Abstract
Adam Smith, asserting the common humanity of the street porter and the philosopher, articulated the classical economists' model of social interactions as exchanges among equals. This model had largely fallen out of favor until, recently, a number of scholars in the avant-garde of economic thought rediscovered it and rechristened it "analytical egalitarianism." In this volume, Sandra J. Peart and David M. Levy bring together an impressive array of authors to explore the ramifications of this analytical ideal and to discuss the ways in which an egalitarian theory of individuality can enable economists to reconcile ideas from opposite ends of the political spectrum. "The analytical egalitarianism project that Peart and Levy have advanced has come to occupy a prominent place in the current agenda of historians of economic thought." ---Ross Emmett, Associate Professor of Economics and Co-Director of the Michigan Center for Innovation and Economic Prosperity, Michigan State University "These essays and dialogs from the Summer Institute would make Adam Smith, economist and moral philosopher, proud." ---J. Daniel Hammond, Hultquist Family Professor of Economics, Wake Forest University With essays by: James M. Buchanan, Alfred Nobel Memorial Prize in Economic Sciences recipient (1985) and Professor Emeritus, George Mason University and Virginia Polytechnic and State University Juan Pablo Couyoumdijian, Universidad del Desearrollo, Chile Tyler Cowen, George Mason University Eric Crampton, University of Canterbury, New Zealand Andrew Farrant, Dickinson College Samuel Hollander, Professor Emeritus, University of Toronto M. Ali Khan, Johns Hopkins University Thomas Leonard, Princeton University Deirdre McCloskey, University of Illinois, Chicago Leonidas Montes, Dean of School of Government, Universidad Adolfo Ibañez, Chile Maria Pia Paganelli, Yeshiva University and New York University Warren J. Samuels, Professor Emeritus, Michigan State University Eric Schliesser, VENI post-doctoral research fellow, Leiden University, and University of Amsterdam Gordon Tullock, George Mason University Sandra J. Peart is Dean of the Jepson School of Leadership Studies, University of Richmond, Virginia. David M. Levy is Professor of Economics at George Mason University (GMU) and Research Associate at the Center for Study of Public Choice at GMU. They are Co-Directors of George Mason University's Summer Institute for the Preservation of the History of Economics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".