Bibliographic record
Abstract
This work is focused on the impact and influence of Dr. Donald D. Horward on studies of the French Revolution and First Empire from 1960-present. It can be used as a lens to understand the development, expansion, and contraction of research, publications, graduate student production, academic dialogue, and outside private interest in the two fields. The study marks the importance of Dr. Horward’s influence on international cooperation and dialogue within the field of Napoleonic studies. It highlights how that influence led to the involvement of national governments in projects dedicated to their history, particularly in France, England, Spain, and Portugal. Horward was the primary engine behind combined academic efforts to expand the reach of military history within studies of Napoleonic Europe. Horward’s expertise, particularly in the Peninsular War, eventually caught the eye of the U.S. military and established a unique link between academia and various service branch schools, not the least of which was West Point, for a quarter century thereafter. This relationship strengthened and burgeoned into a dynamic sector within the broader field of Napoleonic studies, as these soldier-scholars not only taught future army officers, but developed into academics in their own right. Finally, Dr. Horward was a major catalyst driving the private funding pumped into the field in the last two decades of the 20th century, just as the fields reached their high tide in production and interest.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".