Bibliographic record
Abstract
This book attempts to solve the mystery of why a noble lieutenant in the French army, who had greeted the French Revolution enthusiastically, turned against it in 1792. A chance discovery of personal papers led Didier Michel to investigate the life of Anne Achille Alexandre, the chevalier de Blondel de Nouainville, an obscure noble officer from Normandy. Michel initially wrote a doctoral thesis at the Université Panthéon-Sorbonne, based on the correspondence of Nouainville in the collections of the Bibliothèque Municipale of Cherbourg. The book under review here is an abbreviated version in the form of a coffee table book whose stated audience is the wider public complete with glossy illustrations and maps. Blondel de Nouainville is of interest to the author because he is celebrated by both sides of the revolutionary divide, initially in Rennes on the journée of 10 May 1788, and later, in 1792, as an émigré hero. The dilemma which Michel attempts to address is why a liberal lieutenant, who defended the constitutional rights and freedoms of the Bretons, die in a British uniform fighting against French republican forces in Flanders in 1793? The author’s method of addressing this question is through the medium of a traditional biography.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".