Bibliographic record
Abstract
The project that became this book developed over many years.I, therefore, owe much to the people who helped make this book possible in the first place.At the University of Toronto, James Retallack provided rigorous and invaluable guidance for this project from the beginning.What began as a story about foreign relations among small German states became much more meaningful because of his influence.Jim taught me how to write and think like a historian.A short paragraph cannot do him justice, but I hope seeing this study in print will start to make good the countless hours that he spent mentoring me.I also owe a great deal to Doris Bergen, whose careful suggestions, insight, and seemingly inexhaustible enthusiasm shaped this book at every step.Doris remains an expert at the well-timed word of encouragement and is a model of pedagogical practice.She has been a source of constant inspiration for me and many other aspiring historians, and I hope I fulfilled some of her hopes for this project.I am extremely grateful that James Brophy of the University of Delaware joined my committee just before the global pandemic waylaid so many other plans.He succeeded in combining sharpness and kindness in his role.Jim Brophy's critiques and questions about both minutia and the big picture turned these chapters into something others might want to read.I would also like to thank Prof. Abigail Green at the University of Oxford for agreeing to serve as the external examiner of my dissertation.Her insights greatly improved this study.My gratitude also goes to Anna Holian at Arizona State University, who first encouraged my interest in German history.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.392 | 0.215 |
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".