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Magnates and Military Contracting in Hungary after 1648

2022· book-chapter· en· W4312266266 on OpenAlexaboutno aff
András Oross

Bibliographic record

VenueBritish Academy eBooks · 2022
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEuropean Political History Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPeacetimeFrontierState (computer science)Quarter (Canadian coin)MonarchyCashPolitical scienceEconomic historyAncient historyHistoryEconomyLawArchaeologyPoliticsBusinessEconomicsFinance

Abstract

fetched live from OpenAlex

In the 1650s about a quarter of the new peacetime standing army of the Habsburg Monarchy (c. 5000 soldiers) served for a time in one of the castles on the Hungarian-Ottoman frontier. Organising the upkeep of these soldiers was coordinated by the central authorities in Vienna with the collaboration of the Bohemian and Austrian lands as well as the Hungarian Estates. The kingdom of Hungary – a theatre of war since the 1520s – participated in the Habsburg fiscal-military state primarily through the provision of resources in kind (grain and wine), rather with taxes paid in cash. Contractors drawn from among Hungarian magnates and military officers played a key role in this business.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.005
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.206
Teacher spread0.184 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2022
Admission routes1
Has abstractyes

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