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Record W3003580926 · doi:10.1017/ytm.2019.6

Sustainability and Indigenous Aesthetics: Musical Resilience in Sámi and Indigenous Canadian Theatre

2019· article· en· W3003580926 on OpenAlexaboutno aff
Klisala Harrison

Bibliographic record

VenueYearbook for Traditional Music · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousFlourishingPoliticsColonialismCultural assimilationGender studiesModernitySociologyAestheticsHistoryPolitical scienceMedia studiesArtLawPsychology

Abstract

fetched live from OpenAlex

Histories of colonial cultural erasure, unsuccessful decolonisation or postcolonialism and rapid modernisation are typically seen as the challenges to sustaining Indigenous traditional musics (Harrison, in press). The Indigenous peoples of Canada have experienced colonial assimilationist policies of government and church, including residential schools that took children away from their families and forbade song, dance and language. These policies resulted in musics and even entire cultures being erased. Although there have been recent improvements in Scandinavia, similar kinds of discrimination happened where the traditional Sámi vocal form, joik (in pan-Sámi juoiggas ) was long (and in some cases, still is) regarded as sinful, and Sámi children were forbidden to use their mother tongues at school (for example, from about 1850 to 1980 during Norway’s Fornorskning or Norwegianisation policy). In recent years, the Indigenous musics of Canada and the Nordic countries, among others, have reflected, articulated and interpellated sociocultural interrelations and politics (Diamond 2002; Diamond et al. 2018; Harrison 2009; Hilder 2012, 2015; Moisala 2007; Ramnarine 2009, 2017), and Indigenous artists have taken action on politicised issues through a range of contemporary and flourishing artistic expressions (Robinson and Martin 2016).

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.989

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 studies0.0000.001
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 head0.075
GPT teacher head0.212
Teacher spread0.137 · 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 designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

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