L'armée de l'an II : la levée en masse et la création d'un mythe républicain
Bibliographic record
Abstract
Alan Forrest, The Army of the Year II in modern memory : the levée-en-masse and the creation of a republican myth The armies of the French Revolution would be a key point of reference for future generations, and would be characterised by a range of different and complementary qualities - by the fact of conscription, of course, the equality of all before the draft, and the requirement to serve in person ; but also human qualities of flair, enthusiasm, generosity of spirit, and patriotism. The soldier is spared the criticism that is reserved for political figures ; he does not share their responsibilities, and continues to enjoy a positive image under such different regimes as the July Monarchy, the Second Empire and the Third Republic. But which is the army that is being remembered ? How is it remembered by future generations ? And why does the myth of the levée en masse, of the response to the call of the patrie en danger, prove so resilient ? This article traces the evolution of the army in public memory, examining the different depictions of the army and of the nation-in-arms that are encountered during the nineteenth century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".