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Record W1472047630

“Страна городов” и ее столица: Новгород в картине мира средневековых скандинавов

2015· article· ru· W1472047630 on OpenAlexaboutno aff
Джаксон Татьяна Николаевна

Bibliographic record

VenueInternational Journal of Slavic Studies · 2015
Typearticle
Languageru
FieldArts and Humanities
TopicHistorical and Archaeological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIcelandicCapital (architecture)HomelandHistoryAncient historyNorwegianQuarter (Canadian coin)ArchaeologyGenealogyClassicsGeographyLawPolitical sciencePoliticsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

The paper presents data preserved in Old Norse-Icelandic literature on Holmgarðr, a place that is traditionally identified with Novgorod. Holmgarðr appears in these writings as a capital of Garðariki (Old Rus’): all Russian princes familiar from these sources have their seat in this place. Almost all the events occurring in Russia are associated in the sagas with Holmgarðr : Scandinavians come to Holmgarðr to seek refuge or service; four Norwegian kings stay in Holmgarðr for a period of time; Scandinavians return to their homeland or sail to distant lands from this place; and Scandinavian merchants also come to this town. Holmgarðr is described in a generalized way. It has the prince’s court, the chamber of the princess, a special hall built for the Varangian guards, the Church of St. Olav, and a marketplace; in other words, we are dealing with a traditional set of characteristics of a capital city. Novgorod on the mental map of medieval Scandinavians belongs to the eastern quarter ( Austrhalfa ) of the oekumene, and to get there travelers had to go austr ‘east,’ to cross the Baltic Sea ( Austmarr, Eystrasalt ), and to pass Ladoga ( Aldeigja, Aldeigjuborg ), where they changed from ocean-going ships to river vessels and where they waited for a guaranty of safe travel ( grið ) from the prince of Novgorod ( konungr i Holmgarði ).

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.242
GPT teacher head0.369
Teacher spread0.127 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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