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Record W2605141061

THE HERITAGE OF THE LIBRARY OF THE NESVIZH RADZIWILLS IN LITHUANIA

2015· article· en· W2605141061 on OpenAlexaboutno aff
Daiva Narbutienė

Bibliographic record

VenueBook Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCentral European Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsLithuanianLibrary scienceSubject (documents)Cultural heritageQuarter (Canadian coin)State (computer science)HistoryPolitical scienceArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The Radziwills, an aristocratic family of the Great Duchy of Lithuania, amassed sizable book collections at their estates. Notable is the Nesvizh castle collection, started in the middle of the XVI century by the Great Chancellor of Lithuania, Mikalojus Radvila (Radziwill) the Black. In 1772, after the first division of the Republic of the Two Nations, this collection – one of the state’s largest personal libraries – was removed to the Russian Academy of Sciences in St. Petersburg. The interest in the cultural heritage of the Nesvizh Radziwills in Lithuania was prompted by the iniciative of the cultural institutions of neighbour countries (Poland, Bielorussia and, to a certain degree, Russia) to study the heritage of the Nesvizh Radziwills, dispersed throughout various countries. The last year, the virtual reconstruction of the Radziwills’ collection in Nesvizh was a subject of international scientific conferences and meetings, and of an international pilot project. In Lithuanian libraries, there are several remaining copies with the ex-librises of the Nesvizh Radziwills: three in the Library of the Lithuanian Academy of Sciences, one each in the libraries of Vilnius University, of the Kaunas University of Technology and of the Kazys Varnelis house-museum. The electronic catalogue of the Nesvizh Radziwills’ library, compiled in the Karol Estreicher Institute of Polish Bibliography, includes, among others, a copy from the Library of the Lithuanian Academy of Sciences.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.004
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.020
GPT teacher head0.257
Teacher spread0.237 · 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 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
Published2015
Admission routes1
Has abstractyes

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