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Record W2611805744 · doi:10.1139/as-2017-0001

Museum cultural collections: pathways to the preservation of traditional and scientific knowledge

2017· article· en· W2611805744 on OpenAlexvenueno aff
Angela J. Linn, Joshua D. Reuther, Chris B. Wooley, Scott Shirar, Jason Rogers

Bibliographic record

VenueArctic Science · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousBarterTraditional knowledgeHistoryCultural artifactMuseologyNatural (archaeology)Environmental ethicsSociologyAnthropologyArchaeologyEcologyBiology

Abstract

fetched live from OpenAlex

Museums of natural and cultural history in the 21st century hold responsibilities that are vastly different from those of the 19th and early 20th centuries, the time of many of their inceptions. No longer conceived of as cabinets of curiosities, institutional priorities are in the process of undergoing dramatic changes. This article reviews the history of the University of Alaska Museum in Fairbanks, Alaska, from its development in the early 1920s, describing the changing ways staff have worked with Indigenous individuals and communities. Projects like the Modern Alaska Native Material Culture and the Barter Island Project are highlighted as examples of how artifacts and the people who constructed them are no longer viewed as simply examples of material culture and Native informants but are considered partners in the acquisition, preservation, and perpetuation of traditional and scientific knowledge in Alaska.

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)0.010
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.007
Science and technology studies0.0200.020
Scholarly communication0.0250.013
Open science0.0020.026
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0340.004

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 head0.180
GPT teacher head0.285
Teacher spread0.105 · 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.

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

Citations6
Published2017
Admission routes1
Has abstractyes

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