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Record W3213200088 · doi:10.2478/mik-2021-0008

Arctic Drama to Sámi Theatre – Cultural Clashes Towards Decolonisation: In Shared Dialogic Spaces

2021· article· en· W3213200088 on OpenAlexaboutno aff
Knut Ove Arntzen

Bibliographic record

VenueArt History & Criticism · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsnot available
Fundersnot available
KeywordsDramaDecolonizationDramaturgyIndigenousHistorySociologyGender studiesEthnologyAestheticsArtLiteraturePolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

Summary This article deals with the concept of Arctic Drama, which is about how there is a relationship between drama and cultural clashes in the perspective of shared cultures in the northern Scandinavian area, which is defined as arctic in the geographical sense. In this vast area the Sámi people historically and to the present day have been living from reindeer herding in a nomadic lifestyle, giving them a close relationship to nature. Norwegians and Swedes colonised this area historically, especially the coast for fishing.There have been strong cultural clashes since the Viking ages, but colonisation mainly started later by introducing Christianity by force in the 16 th century. Since the Romantic age, these ethno-cultural clashes have been reflected in drama and theatre, and some plays by Henrik Ibsen and Knut Hamsun echo these tensions. An independent theatre of the Sámi people as well as of other indigenous people in Greenland and Canada, like the Inuits, would also develop some theatrical strategies based in a dramaturgy that could be described as a “spiral dramaturgy”. Cultural independence has contributed to a decolonisation process, contributing to even out the cultural clashes in theatre and drama, which could be defined as postcolonial towards decolonisation. This article focuses on the area of arctic Scandinavia.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.780
Threshold uncertainty score0.997

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)0.0040.001

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 head0.085
GPT teacher head0.277
Teacher spread0.191 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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