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Record W2730446150 · doi:10.14430/arctic4658

Moravian and Inuit Encounters: Transculturation of Landscapes and Material Culture in West Greenland

2017· article· en· W2730446150 on OpenAlexvenueaboutno aff
Peter Andreas Toft

Bibliographic record

VenueARCTIC · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHistorical and Cultural Archaeology Studies
Canadian institutionsnot available
FundersKommissionen for Videnskabelige Undersøgelser i GrønlandVelux Stiftung
KeywordsSubsistence agricultureGeographySettlement (finance)ArchaeologyEthnologyHuman settlementStyle (visual arts)HistoryAgriculture

Abstract

fetched live from OpenAlex

From 1733 to 1900, Moravian missionaries settled in West Greenland to missionize and teach. These activities resulted in local mission and settlement layouts that followed Moravian principles and at the same time adapted to local landscapes and Inuit traditions and subsistence practices. This article explores spatial data, objects, oral tradition, and written sources from sites at Uummannaq, Akunnaat (Lichtenfels), and Kangillermiut, West Greenland. At these sites, landscape use was transculturated, and the material culture changed among both the European missionaries and the local Inuit. Moravian missionaries traded European commodities for Inuit artefacts, and an Inuit industry evolved through creating souvenirs for the missionaries. At the same time, local Inuit material culture was influenced by the presence of the Moravians, who introduced written language, administrative birth numbers, goats, and new crafts such as European-style basket weaving. The cultural encounters at the three Greenlandic sites resulted in objects and practices that were cultural composites of European and Inuit ideals and traditions.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.994

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)0.0000.000

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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 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

Citations4
Published2017
Admission routes2
Has abstractyes

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