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Milton Friedman

2016· book· en· W2345454087 on OpenAlexaboutno aff

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsnot available
Fundersnot available
KeywordsMonetarismVariety (cybernetics)George (robot)EconomicsPositive economicsPolitical scienceMonetary policyKeynesian economicsHistoryArt history

Abstract

fetched live from OpenAlex

Milton Friedman is widely regarded as one of the most influential economists of the twentieth century. Although he made many important contributions to both economic theory and policy - most clearly demonstrated by his development of and support for monetarism - he was also active in various spheres of public policy, where he more often than not pursued his championing of the free market and liberty. This volume assesses the importance of the full range of Friedman's ideas, from his work on methodology in economics, his highly innovative consumption theory, and his extensive research on monetary economics, to his views on contentious social and political issues such as education, conscription, and drugs. It also presents personal recollections of Friedman by some of those who knew him, both as students and colleagues, and offers new evidence on Friedman's interactions with other noted economists, including George Stigler and Lionel Robbins. The volume provides readers with an up to date account of Friedman's work and continuing influence and will help to inform and stimulate further research across a variety of areas, including macroeconomics, the history of economic thought, as well as the development and different uses of public policy. With contributions from a stellar cast, this book will be invaluable to academics and students alike. Contributors to this volume - Robert D. Auerbach,The University of Texas at Austin Nicholas Barr, London School of Economics Vincent Barnett, Research Fellow William A. Barnett, University of Kansas and Center for Financial Stability Peter J. Boettke, George Mason University Lawrence A. Boland, Simon Fraser University Michael D. Bordo, Rutgers University Michael J. Boskin, Stanford University Marcel Boumans, University of Amsterdam and Erasmus University Rotterdam Russell S. Boyer, University of Western Ontario Rosolino Candela, George Mason University Gregory C. Chow, Princeton University Robert A. Cord, researcher in economics Selwyn Cornish, Australian National University Harris Dellas, University of Bern Gerald P. Dwyer, Clemson University Neil R. Ericsson, Federal Reserve System Craig Freedman, The Australian School of Business Roger W. Garrison, Auburn University J. Daniel Hammond, Wake Forest University Arnold C. Harberger, University of California, Los Angeles G.C. Harcourt, University of Cambridge and University of Adelaide David F. Hendry, Oxford University Robert L. Hetzel, Federal Reserve Bank of Richmond Stedman B. Hood, PlateJoy Inc. Susan Howson, University of Toronto Douglas A. Irwin, Dartmouth College Morris M. Kleiner, University of Minnesota Peter Kriesler, University of New South Wales Eugene Lerner, Hightower Investment Advisors James R. Lothian, Fordham University Robert E. Lucas, Jr., University of Chicago Bennett T. McCallum, Carnegie Mellon University Allan H. Meltzer, Carnegie Mellon University and Stanford University Patrick Minford, Cardiff University Edward Nelson, University of Sydney J.W. Nevile, University of California, Berkeley Thomas I. Palley, AFL-CIO Sylvie Rivot, University of Mulhouse John J. Seater, Boston College Richard Selden, University of Virginia John D. Singleton, Duke University Scott B. Sumner, Bentley University George S. Tavlas, Bank of Greece Mark Thornton, Ludwig von Mises Institute Paul Turpin, University of the Pacific K. Vela Velupillai, University of Trento

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.065
Threshold uncertainty score0.219

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0070.005
Open science0.0010.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0650.039

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.

Opus teacher head0.030
GPT teacher head0.178
Teacher spread0.148 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations16
Published2016
Admission routes1
Has abstractyes

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