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Record W2528269316

The sound of ruins: Sigur Ros' Heima and the post-rock elegy for place

2012· other· en· W2528269316 on OpenAlexaboutno aff
Lawson Fletcher

Bibliographic record

VenueSwinburne Research Bank (Swinburne University of Technology) · 2012
Typeother
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsElegySound (geography)ArtGeologyLiteraturePoetryGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

Amongst the ways in which it maps out the geographical imagination of place, music plays a unique role in the formation and reformation of spatial memories, connecting to and reviving alternative times and places latent within a particular environment. Post-rock epitomises this: understood as a kind of negative space, the genre acts as an elegy for and symbolic reconstruction of the spatial erasures of late capitalism. After outlining how post-rock’s accommodation of urban atmosphere into its sonic textures enables an ‘auditory drift’ that orients listeners to the city’s fragments, the article’s first case study considers how formative Canadian post-rock acts develop this concrete practice into the musical staging of urban ruin. Turning to Sigur Ros, the article challenges the assumption that this Icelandic quartet’s music simply evokes the untouched natural beauty of their homeland, through a critical reading of the 2007 tour documentary Heima. A closer reading of the band’s audiovisual practice reveals a counter-geography of Iceland, in which the country’s decaying industrial past is excavated and its more recent ecological failures are accounted for. As with post-rock more generally, this proposes a more complex relationship between music, place and memory than that offered by notions of reflection and nostalgia, which instead emerges as a melancholic mourning for spatial pasts.

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 categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.324
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 studies0.0020.006
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.031
GPT teacher head0.248
Teacher spread0.216 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2012
Admission routes1
Has abstractyes

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