Sustainability and Indigenous Aesthetics: Musical Resilience in Sámi and Indigenous Canadian Theatre
Bibliographic record
Abstract
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-Sámijuoiggas) 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’sFornorskningor 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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.031 | 0.028 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".