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Record W3156949893

A Gathering of Names: On the Categories and Collections of Siberian Shamanic Materials in Late Imperial Russian Museum, 1880-1910

2019· dissertation· en· W3156949893 on OpenAlexfundno aff
Marisa Karyl Franz

Bibliographic record

VenueTSpace · 2019
Typedissertation
Languageen
FieldArts and Humanities
TopicReligious Studies and Spiritual Practices
Canadian institutionsnot available
FundersFaculty of Education, Victoria University of WellingtonSocial Sciences and Humanities Research Council of CanadaUniversity of Toronto
KeywordsArchaeologyHistoryToponymyGeographyAncient history
DOInot available

Abstract

fetched live from OpenAlex

This dissertation is an intellectual history of the ethnographic naming and systematising of Siberian shamanic materials by collectors for late imperial Russian museums, between approximately 1880-1910. The late imperial era was a time of social and political change in the Russian Empire, during which there was a dramatic increase in the number of local Siberian museums founded. This project approaches Siberia and the local Siberian museums within the context of late imperial Russian scientific modernity to argue that these museums were constructing a new local Siberian intellectual and scientific network of researchers who were defining shamanism through their collections. This project focuses on collectors and the museum communities in the cities of Yakutsk, Irkutsk, and St. Petersburg. It approaches these sites as increasingly interconnected through the academic and personal networks and infrastructural developments that brought increasing numbers of people, willingly and unwillingly, to Siberia at the turn of the century. Specifically looking at collection programmes, a form of desiderata, this dissertation traces what types of objects and categories of information were understood as shamanic in order to explore how the category circulated and became defined within the ethnographic museums in Siberia and in European Russia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.284
Teacher spread0.259 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations1
Published2019
Admission routes1
Has abstractyes

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