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Record W2902684778

Looking for a Place to Happen: Collective Memory, Digital Music Archiving, and the Tragically Hip

2018· article· en· W2902684778 on OpenAlexaboutno aff
Alan Galey

Bibliographic record

VenueArchivaria (Association of Canadian Archivists) · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsnot available
Fundersnot available
KeywordsAmateurContext (archaeology)DocumentationCollective memoryHistoryArtVisual artsComputer sciencePolitical scienceLawArchaeology
DOInot available

Abstract

fetched live from OpenAlex

This article takes the Canadian band the Tragically Hip as a case study in pro-amateur digital music archiving and also considers the larger place of the band's music within the context of cultural memory.After lead singer and lyricist Gord Downie's cancer diagnosis in 2016, the band made the remarkable decision to undertake a Canadian tour, which became one of the most obsessively documented rock tours in Canadian history.Drawing on close readings of the band's performances and some of the digital records that capture them, the article argues for the importance of considering questions of evidence and memory together, especially in the context of archiving popular music.The article begins with a discussion of the Tragically Hip's value as an archival case study, which is based on the band's linking of composition and live improvisation.It then turns to the engagement of the band's songs with the materials of Canadian collective memory, the work of bootleg collectors and pro-am archivists in the documentation of performance history, and the documentation of the band's final Canadian tour in 2016.Throughout, the article examines how professional and pro-am archiving can complement each other in the case of a band like the Hip, whose music is so closely linked to the idea of Canadian collective memory. Looking for a Place to Happen

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.946
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

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

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 head0.015
GPT teacher head0.190
Teacher spread0.175 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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