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Record W2909701019 · doi:10.1525/jpms.2018.300412

Southern Sounds, Northern Voices

2018· article· en· W2909701019 on OpenAlexaffabout
Ryan Shuvera

Bibliographic record

VenueJournal of Popular Music Studies · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsWestern University
Fundersnot available
KeywordsIndigenousCountryStudioWhite (mutation)HistoryColonialismFolk musicVisual artsEthnologyArchaeologyArt

Abstract

fetched live from OpenAlex

Wilf Carter (Montana Slim) crossed the Canadian-U.S. border in 1935 to further his career as a country musician. Hank Snow moved to Nashville in 1945, reaching the stage of the Grand Ole Opry in 1950. Twenty-one years later Neil Young settled into Nashville’s Quadraphonic Sound Studio to record songs that would be featured on the album Harvest. Today, Nashville’s New West Records represents country-inspired Canadian musicians Daniel Romano and Corb Lund. These artists make up part of a notable history of northerners blending North American identities through country music. A significant and overlooked part of this history came to light in 2014 with the release of the Native North America (Vol. 1): Aboriginal Folk, Rock, and Country 1966-1985 compilation from Light In The Attic Records. NNA (Vol. 1) is a collection of limited releases from Indigenous musicians from across Canada and Alaska. It is significant because it makes audible that Indigenous musicians performed—and continue to perform—country, folk, and rock music, challenging the borders and identities forced on them through settler-colonialism. These artists bring together southern sounds and northern voices—often using northern Indigenous languages—to articulate different experiences under North American colonization. This paper begins to explore how artists such as Willie Dunn, John Angaiak, and William Tagoona unsettle North American boundaries and identities through country music. This paper also begins to explore the opportunities and challenges this compilation presents to white settler listeners.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score1.000

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.081
GPT teacher head0.268
Teacher spread0.187 · 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

Citations10
Published2018
Admission routes2
Has abstractyes

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