Probability and Statistics: Essays in Honor of David A. Freedman
Bibliographic record
Abstract
This volume is our tribute to David A. Freedman, whom we regard as one of the great statisticians of our time. He received his B.Sc. degree from McGill University and his Ph.D. from Princeton, and joined the Department of Statistics of the University of California, Berkeley, in 1962, where, apart from sabbaticals, he has been ever since.\n¶\nIn a career of over 45 years, David has made many fine contributions to probability and statistical theory, and to the application of statistics. His early research was on Markov chains and martingales, and two topics with which he has had a lifelong fascination: exchangeability and De Finetti’s theorem, and the consistency of Bayes estimates. His asymptotic theory for the bootstrap was also highly influential. David was elected to the American Academy of Arts and Sciences in 1991, and in 2003 he received the John J. Carty Award for the Advancement of Science from the U.S. National Academy of Sciences.\n¶\nIn addition to his purely academic research, David has extensive experience as a consultant, including working for the Carnegie Commission, the City of San Francisco, and the Federal Reserve, as well as several Departments of the U.S. Government–Energy, Treasury, Justice, and Commerce. He has testified as an expert witness on statistics in a number of law cases, including Piva v. Xerox (employment discrimination), Garza v. County of Los Angeles (voting rights), and New York v. Department of Commerce (census adjustment).\n¶\nLastly, he is an exceptionally good writer and teacher, and his many books and review articles are arguably his most important contribution to our subject. His widely used elementary text Statistics, written with R. Pisani and R. Purves, now in its 4th edition, is rightly regarded as a classic introductory exposition, while his second text Statistical Models (2005) is set to become just as successful in its field.\n¶\nThe roles of theoretical researcher, consultant, and expositor are not disjoint aspects of David’s personality, but fully integrated ones. For over 20 years now, he has been writing extensively on statistical modeling. He has contributed to theory, and prepared illuminating expositions and given penetrating critiques of old and new models and methods in a wide range of contexts. The result is a quite remarkable body of research on the theory and application of statistics, particularly to the decennial U.S. census, the social sciences (especially econometrics, political science and the law), and epidemiology. These themes are reflected in this volume of papers by friends and colleagues of David’s. We’d like to thank him for his wonderful body of work, and to wish him well for the future.
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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 | 0.011 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".