Das Bild des Anderen in den Straßennamen von Mühlbach. Siculorumgasse, Griechengasse, Opricestengasse, Str. Saxonii Noi
Bibliographic record
Abstract
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).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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.
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".