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Record W3198668408 · doi:10.3167/arcs.2021.070105

Listening to Terror Soundscapes

2021· article· en· W3198668408 on OpenAlex
Luis Velasco-Pufleau

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueConflict and Society · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
Fundersnot available
KeywordsActive listeningSoundscapeTerrorismMnemonicMeaning (existential)PsychologySpace (punctuation)Event (particle physics)Affect (linguistics)Sound (geography)HistorySocial psychologyCognitive psychologyCommunicationLinguisticsAcoustics

Abstract

fetched live from OpenAlex

Listening experiences provide valuable insights in understanding the meaning of events and shaping the way we remember them afterwards. Listening builds relationships with places and subjectivities. What kinds of relationships and connections are built through listening during an event of extreme violence, such as a terrorist attack? This article examines the relationships between sound, space, and affect through an acoustemology of Bataclan survivors’ sensory experiences of both the terrorist attack and its aftermath. I draw on the testimonies of nine survivors of the Bataclan terrorist attack in Paris, which unfolded on the evening of 13 November 2015 during a rock concert, as well as interviews with three parents of survivors and victims. This article explores how the study of listening experiences and aural memories of survivors contributes to understanding mnemonic dynamics and processes of recovery related to sound following violent events.

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.

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 score0.885
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.269
Teacher spread0.231 · 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