Bibliographic record
Abstract
The first reference work to explore the 2000-year history of political realism and reassess its place in today’s world Download an ebook of the chapter abstracts and notes on contributors (pdf) Political realism is a highly diverse body of international relations theory. This substantial reference work examines political realism in terms of its history, its scientific methodology and its normative role in international affairs. Split into three sections, it covers the 2000-year canon of realism: the different schools of thought, the key thinkers and how it responds to foreign policy challenges faced by individual states and globally. It brings political realism up-to-date by showing where theory has failed to keep up with contemporary problems and suggests how it can be applied and adapted to fit our new, globalised world order. Key Features The first volume to offer a full, balanced guide to Political Realism: its history and its normative role in international affairs Covers the main thinkers, from Thucydides through Niccolò Machiavelli to Isaiah Berlin Engages with the major foreign policy issues of our times, such as strategic deterrence, nationalism, terrorism, cyber security, climate change, the open society and religion Considers political realism in non-Western contexts, including Israel, Russia and China Includes political realism’s ground-up growth and interpretation outwith Western contexts Contributors Uriel Abulof , Tel-Aviv University, Israel. Christopher Adair-Toteff , Zeppelin University, Germany. Erica Benner , Yale University, USA. John Bew , King’s College London, UK. Todd Breyfogle , Aspen Institute, Washington, D.C., USA. Joshua Cherniss , Georgetown University, USA. Alan Chong , S. Rajaratnam School of International Studies, Singapore. Lindsay P. Cohn , U.S. Naval War College, USA. Kody W. Cooper , University of Tennessee at Chattanooga, USA. Marzieh Kouhi Esfahani , Durham University, UK. Markus Fischer , California State University, Fullerton, USA. Richard Forno , University of Maryland, Baltimore County, USA. Stuart Gray , Politics at Washington and Lee University, USA. Robert Howse , New York University School of Law, USA. David Martin Jones , University of Queensland, Australia and King’s College London, UK. Menno R. Kamminga , University of Groningen, Netherlands. Peter Iver Kaufman , University of North Carolina, Chapel Hill and University of Richmond, USA. David Kerr , Durham University, UK. Paul Kirkland , Carthage College, Wisconsin, USA. Douglas B. Klusmeyer , American University, Washington, DC, USA. Konstantinos Kostagiannis , University of Maastricht, Netherlands. Ayelet Haimson Lushkov , University of Texas at Austin, USA. Cecelia Lynch , University of California, Irvine, USA. David Mayers , Boston University, USA. Kenneth B. McIntyre , Sam Houston State University in Huntsville, Texas, USA. Neville Morley , University of Exeter, UK. John Mueller , Mershon Center for International Security Studies and Ohio State University, USA. Masashi Okuyama , International Geopolitical Institute Japan and Aoyama Gakuin University, Japan. Samir Puri , King's College London, UK. Grace Roosevelt , Metropolitan College of New York, USA. Felix Rösch , Coventry University, UK. William E. Scheuerman , Indiana University, USA. Tim Spiekerman , Kenyon College, Ohio, USA. Peter Stirk , Durham University, UK. Tom Switzer , Centre for Independent Studies in Sydney. Zhao Tingyang , Chinese Academy of Social Sciences in Beijing, China. Jodok Troy , University of Innsbruck, Austria. Alex Wilner , Carleton University, Ottawa, Canada. Derek M. C. Yuen , University of Hong Kong. Rashed Uz Zaman , University of Dhaka, Bangladesh. "
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.105 | 0.034 |
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".