Bibliographic record
Abstract
As the name historical social topography implies it comprehends the ancient location and distribution of particular groups and layers of inhabitants in a settlement. It is important since ethnic, religious and occupational groups are able either to impose particular characters of settlement structure, or significantly influence their location and ground use. Several social data of a Christian tithe list made for the diocese of Bács in the year 1522, and a defter (i.e. a Turkish tax list) from 1546 were placed on the medieval map of Szeged previously reconstructed by the author - resulting in an extremely rich social topographic picture of a large medieval peasant market town. It can be observed that the well-heeled intelligentsia and the wealthy burgesses, - priests, judges, schoolmasters etc, and the craftsmen of privileged trades such as goldsmiths, and the vineyard owners - lived near the centres, mainly in the fortified Palánk or around the churches in Felsõváros and Alsóváros. It is obvious that those, whose trades were connected with agriculture or animal husbandry, lived on the outskirts, making use of good transportation and storage possibilities there. The fine manufacturers and the ones working with great value were clustering in certain areas, probably as a consequence of the guild system. The processed and mapped 411 data of 403 tax payers cover more than a quarter of the 1574 listed households.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".