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Record W2320238818 · doi:10.1017/s0032247416000115

Rediscovering lost relationships: Canadian Arctic ethnographic materials recovered from the ‘ghost ship’ <i>Baychimo</i> and the University of Alaska Museum of the North

2016· article· en· W2320238818 on OpenAlexaboutno aff
Joshua D. Reuther, Jason Rogers

Bibliographic record

VenuePolar Record · 2016
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsArchaeologyBayCrewGeographyArcticThe arcticEthnographyHistoryOceanographyGeology

Abstract

fetched live from OpenAlex

ABSTRACT In 1931, the Hudson's Bay Company cargo steamer, SS Baychimo , was trapped in sea ice and abandoned in the Chukchi Sea off the northern coast of Alaska. Large amounts of scientific and navigational instruments and gear and personal items were left aboard, among them an ethnographic collection gathered in 1930 from Inuit groups in the Canadian Arctic by Richard Sterling Finnie. The ship was boarded several times over the next three years with items being salvaged by locals from nearby Wainwright and Barrow. In 1933, crew and passengers from MS Trader , a small trading vessel from Nome, boarded the abandoned ship and recovered several of Finnie's ethnological specimens. In 1934, Peter Palsson, crewmember of Trader , gave several ethnological specimens to members of the United States Department of the Interior-Alaska College Archaeological Expedition. That year, the Baychimo collection was accessioned to the nascent University of Alaska Museum (now, the University of Alaska Museum of the North). For over 80 years, the collection's relationships with Finnie, the Baychimo , and Palsson remained obscure, and its historical significance has just been rediscovered. This paper describes the collection and the path it took from the Baychimo to the University of Alaska Museum.

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 score0.287
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
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.254
Teacher spread0.222 · 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
Published2016
Admission routes1
Has abstractyes

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