Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 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".