COMMENTS ON DISSERTATIONS SELECTED AS FINALISTS FOR THE GERSCHENKRON PRIZE
Bibliographic record
Abstract
I feel especially honored to be handling the Gerschenkron prize for these meetings on the American Century because I have concluded that the Gerschenkron prize is a quintessentially American beast. This may seem an odd statement; the prize is named for a man who was bron in Russia, trained in Austria, and spoke more than a dozen languages. None of these traits sound quintessentially American. But the prize we have named for him reveals some profound national characteristics. To see my point you have to recall that a dissertation is eligible for the Gerschenkron prize only if it is not eligible for the Nevins prize. We often say that the Nevins prize is for North American economic history, but if you check the precise definition it is really for the economic history of the United States and Canada, thus illustrating our characteristically weak grasp of geography. North America, my encyclopedia tells me, includes all the lands north of the Isthmus of Panama. There is a very large country well to the north of Panama and just to the south of the United States, but for some reason dissertations on this country do not qualify for our prize in North American economic history. But the most revealingly American feature of the Gerschenkron prize is the way it is defined as “not American.” The prize is not for European, or Latin American, or Asian economic history; it is for the rest of the world, or as one of my uncles would put it, “them foreigners over there.”
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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 | 0.022 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.173 | 0.121 |
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".