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Record W3019765746 · doi:10.11575/prism/37711

"The world speaks, I can only listen": Representations of Indigenous Collective Trauma through Film Sound Design

2020· dissertation· en· W3019765746 on OpenAlexaboutno aff
Cancino González

Bibliographic record

VenuePRISM (University of Calgary) · 2020
Typedissertation
Languageen
FieldArts and Humanities
TopicDiverse Musicological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSound (geography)IndigenousSound designMedia studiesVisual artsCommunicationArtAcousticsSociologyPhysics

Abstract

fetched live from OpenAlex

This research project investigates the role of film sound design in the representation of collective trauma in films about experiences and issues faced by Indigenous peoples. In filmmaking, sound is not only fundamental in providing an aura of realism and in evoking affective forces. It can also provide spectators with imagined sound worlds that represent diverse experiences through aural aesthetic elements that in turn create shades of meaning. A transcultural approach in this study integrates the corpus, placing film representations from different countries in conversation through the analysis of their aural compositions. The project analyzes three contemporary films released between 2013 and 2015: Charlie’s Country by director Rolf De Heer (Australia 2013), Rhymes for Young Ghouls by Jeff Barnaby (Canada 2013) and El Abrazo de la Serpiente by Ciro Guerra (Colombia 2015). The findings of this project establish the connection between aural elements, sound mixing, and factors in the representation of collective trauma, such as issues of space, memory, emotion, and expressions of physical pain. By addressing sound not as an isolated subject, but rather as embedded in a context that produces political representations of trauma, this project contributes to the growing understanding of the evocative potential of film sound.

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 categoriesScience and technology studies, 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 score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.072
GPT teacher head0.236
Teacher spread0.164 · 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
Published2020
Admission routes1
Has abstractyes

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