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Under the Shade of the two Eagles. Museum Collection of Polish Researchers and Travelers in Peter the Great Museum of Anthropology and Ethnography (Kunstkamera) of the Russian Academy of Sciences

2019· article· en· W2967770524 on OpenAlexaboutno aff

Bibliographic record

VenueEtnografia · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicPolish Historical and Cultural Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEthnographyPeriod (music)Quarter (Canadian coin)PoliticsValue (mathematics)HistoryAnthropologySociologyArchaeologyPolitical scienceArtLaw

Abstract

fetched live from OpenAlex

A C T. The collections of Polish researchers and travelers in Peter the Great Museum of Anthropology and Ethnography (Kunstkamera) of the Russian Academy of Sciences include ethnographic, anthropological, archaeological and visual photographic materials. Polish scholars contributed to the development of Russian academic studies from the beginning of the nineteenth to the first quarter of the twentieth century, especially in the field of study and exploration of North Asia. This was influenced by the history of interaction between Poland and Russia, as well as the political, social and economic situation in that historical period. Polish researchers can be divided into two groups: exiles and convicts (most often they did not have academic schooling) and researchers who were originally in the service of Russia (usually with a good education received in the territory of the former Poland or Russia). 24 collections with a total of more than 2 200 items were revealed. Most of them are little-known collections. In addition to collections, the Museum's archive contains unique manuscripts of these scholars. Research of all the collections, materials and archives of Polish researchers and travelers in the Museum has great academic value and provides an opportunity for cooperation between Polish and Russian museums and academic institutions, as well as scholars.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.011
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 head0.078
GPT teacher head0.363
Teacher spread0.286 · 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
Published2019
Admission routes1
Has abstractyes

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