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Record W3208106668 · doi:10.18254/s207987840017120-1

The Middle Ages in the Landscape of the Present-Day Pereslavl-Zalessky

2021· article· en· W3208106668 on OpenAlexaboutno aff
Elena Shaduntc

Bibliographic record

VenueIstoriya · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Socio-Economic Development Trends
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Plan (archaeology)TRACE (psycholinguistics)Middle AgesHistoryGeographyArchaeologyPresent dayPopulationArchaeological evidenceAncient historyDemographySociologyLinguistics

Abstract

fetched live from OpenAlex

Among ancient Russian towns, Pereslavl-Zalessky stands out for the rare preservation of its historical town-planning structure. The basis for such estimation is provided by the evidence of sources on the town’s history during the 12th — 17th centuries and a comparative analysis of cartographical documents of the early modern time. The views on the formation of Old Russian towns and assessment of the factors affecting the town-planning have recently undergone considerable changes. The comparison of historical and archival as well as archaeological evidence with the present-day topography of Pereslavl allows us to trace the modification of the planning structure that has retained not only separate architectural objects of the 12th — 17th centuries but also parcels of the medieval town layout. The article presents examples of the ‘exceptions to the rule’ during the execution of a regular plan at the end of the 18th century that, together with historical evidence on the composition and occupations of the trading quarter’s population, permit to more precisely determine the historical peculiarity of Pereslavl.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.051
GPT teacher head0.285
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2021
Admission routes1
Has abstractyes

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