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Record W2046745039 · doi:10.3311/pp.ar.2009-1.04

The medieval social topography of Szeged

2009· article· en· W2046745039 on OpenAlexaboutno aff
Zsolt Máté

Bibliographic record

VenuePeriodica Polytechnica Architecture · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicHungarian Social, Economic and Educational Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSettlement (finance)PeasantEthnic groupIntelligentsiaGeographyQuarter (Canadian coin)ArchaeologyDistribution (mathematics)HistoryGenealogyAncient historyEconomic historyBusinessSociologyPolitical scienceAnthropologyLawPayment

Abstract

fetched live from OpenAlex

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.

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
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.826
Threshold uncertainty score0.998

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 studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.301
Teacher spread0.288 · 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
Published2009
Admission routes1
Has abstractyes

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