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Record W4387051769 · doi:10.29178/nevtert.2008.6

Kaphatta-e nevét a váci Burgundia német telepesekről?

2008· article· en· W4387051769 on OpenAlexaboutno aff
Zoltán Dóra

Bibliographic record

VenueNévtani Értesítő · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicHungarian Social, Economic and Educational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGermanQuarter (Canadian coin)TurkishDowntownHuman settlementHistoryFrontierGeographySmall townAncient historyEthnologyArchaeologyEconomic historySociologySocioeconomicsPhilosophy

Abstract

fetched live from OpenAlex

Burgundia, as the name of a district or a street, can be found in several settlements in Hungary. Despite prevailing assumptions, the true origin of the name is still obscure. On the basis of the findings of Ignác Tragor, an early 20th century researcher of local history, the Burgundia of the town of Vác is often connected by scholars to the German newcomers who settled in Hungary during the reigns of Géza and St. Stephen. Since at that time German settlers had not yet arrived in the town of Vác, this explanation is highly unlikely. After the Mongol invasion of the country (1241–42) and especially after the Turkish occupation of Hungary (1541–1686/99), however, Germans did immigrate to Vác, but settled down in the northern part of the town. This quarter was known as Német város ‘German town’, whilst the district inhabited by Hungarians was called Magyar város ‘Hungarian town’. The downtown Burgundia was established by parcelling its land out in the last third of the 18th century, by which time the Hungarian and the German populations in the districts of the town had been exchanged, resulting in the German inhabitants’ living in the southern quarter of Vác. Relying on this fact, the author concludes that in Vác the name Burgundia might have connections with German settlers, though further evidence is required to gain certainty.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.483
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.082
GPT teacher head0.325
Teacher spread0.243 · 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; both teacher heads agree on what is shown here.

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
Published2008
Admission routes1
Has abstractyes

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