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Record W7018279561

Das Bild des Anderen in den Straßennamen von Mühlbach. Siculorumgasse, Griechengasse, Opricestengasse, Str. Saxonii Noi

2015· article· de· W7018279561 on OpenAlexaboutno aff

Bibliographic record

VenuePublication Server of Goethe University Frankfurt am Main (Goethe University Frankfurt) · 2015
Typearticle
Languagede
FieldSocial Sciences
TopicCentral European and Russian historical studies
Canadian institutionsnot available
Fundersnot available
KeywordsBoroughEthnic groupQuarter (Canadian coin)GermanPopulationMiddle Ages
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study is to reconstruct and document the image of “The Other’’ starting with the historical street names in the Transylvanian town of Sebeş, Alba County, founded in the thirteenth century by German settlers. Due to the fact that, throughout Middle Ages, one of the criteria of naming the streets of a borough was, inter alia, the ethnic one, the street names of the town reveal the ethnic groups which would form the population of the town: Székelys (Siculorumgasse), Saxons (Sachsgasse, Herrengasse, Petrigasse a.s.o.), Romans (Opricestengasse, Suseni– and Joseni Viertel), Greek and Macedonian, as well as Germans from the Southwestern Germany and Austria, who founded the north quarter of the town, in the eighteenth century (Saxonii Noi Street, Saxonii Vechi Street, Quer Gasse). In Sebeş, the street names established after the specific place the road leads the way to also contribute to the image of “The Other’’ (Petersdorfer Gässchen, Daiagasse and Hermannstädter Straße). Furthermore, the names of various local or super regional personalities who influenced the existence of the town also have an important contribution. Examples to illustrate this aspect are particularly the street names from the early stalinist period of communism in Romania (Stalin Street, V. I. Lenin Street, Miciurin Street, Malinovski Street, Rosa Luxemburg Street).

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.005
Science and technology studies0.0020.003
Scholarly communication0.0000.003
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.249
Teacher spread0.209 · 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
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
Published2015
Admission routes1
Has abstractyes

Explore more

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