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Record W7067954858

Music and Ideas of North

2021· book· en· W7067954858 on OpenAlexaboutno aff

Bibliographic record

VenueHuddersfield Research Portal (University of Huddersfield) · 2021
Typebook
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsThe ImaginaryMusicalFocus (optics)DreamExpression (computer science)Popular musicMusical expression
DOInot available

Abstract

fetched live from OpenAlex

Northern identities in different regions, cultures and communities – particularly when constructed as foils to 'The South' – have been the focus of considerable attention among cultural historians, literary scholars and commentators, several of whom are represented in this collection. Yet despite its prominence in the discourse of north-south relations, the role of music in producing and articulating notions of northernness has not been discussed in detail. Rugged yet fragile, communal yet solitary, conservative yet radical, the real and imaginary spaces of the north have inspired many different musical responses, perhaps the most enigmatic coming from pianist Glenn Gould in his radio documentary The Idea of North (CBC, 1967): 'I've read about it, written about it, and even pulled up my parka once and gone there. Yet like all but a very few Canadians I've had no real experience of the North. I've remained, of necessity, an outsider. And the North has remained for me, a convenient place to dream about, spin tall tales about, and, in the end, avoid.' This collection represents the first extended dedicated exploration of music and ideas of north, drawing on northern English, Scottish, Canadian, Scandinavian and Finnish identities, as well as north-south dynamics in a European context, to uncover connections and contradictions in the musical experience and expression of northernness across the globe.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.3890.000

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 head0.135
GPT teacher head0.381
Teacher spread0.246 · 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 designQualitative
Domainnot available
GenreOther

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

Citations0
Published2021
Admission routes1
Has abstractyes

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