Bibliographic record
Abstract
Introduction, Roger E. Backhouse, Birmingham University and Jeff Biddle, Michigan State University The Normative Economics of Statistical Quality Control in WWII, Judy Klein Atomic Energy and the Application of Early Models of Technological Chance in Economics, 1946-1954, Warren Young, Bar Ilan University, Israel The Rationality of Economic Forecasts, Robert Goldfarb, George Washington University and Herman Stekler, George Washington University The Very Idea of Applying Economics: the Modern Minimum-Wage Controversy and Its Antecedents, Thomas C. Leonard, Princeton University On the Concept of Applied Economics: Lessons from Cambridge Economics and the History of Growth Theories, Flavio Comim, St. Edmunds College, Cambridge Political Economy and Applied Economics: The Scottish Tradition in the Twentieth-Century, Alistair Dow, Glasgow Caledonian University, Sheila Dow, Glasgow Caledonian University, and Alan Hutton, Glasgow Caledonian University Strategic Games: From Theory to Application, Robert W. Dimand, Brock University, Ontario Personnel/Human Resource Management: Its Roots as Applied Economics, Bruce E. Kaufman, Georgia State University The Emergence of a New Branch of Economics: The Economics of Education, Pedro Nuno Teixeira, University of Porto, Portugal Public Choice Analysis as a Case Study in the Professionalization of Economics, Steven G. Medema, University of Colorado at Denver Economic Geography and Economic Policy in Twentieth-Century North America, Steven Meardon, Williams College
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 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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".