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Record W7111431152

DDH: A Historical Life

2019· dissertation· en· W7111431152 on OpenAlexaboutno aff

Bibliographic record

VenueDigiNole (Florida State University) · 2019
Typedissertation
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEmpirePrivate sectorWork (physics)Service (business)Field (mathematics)Quarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.421
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.227
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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