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Record W4307637300 · doi:10.3167/proj.2022.160303

Sound Anchors

2022· article· en· W4307637300 on OpenAlex
Brad Jackson

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

VenueProjections · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMedia Influence and Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNarrativeMultimodalityMeaning (existential)Construct (python library)Sound (geography)PsychologyCognitionMeaning-makingCognitive scienceAestheticsSociologyCommunicationLinguisticsArtComputer scienceAcousticsPhilosophy

Abstract

fetched live from OpenAlex

When watching a film, we engage with much more than combinations of moving images. We combine what we see with what we hear, and what we hear often aids in the construction of a story. Although some researchers endorse the ways sound guides viewer expectations, there is still a need to explain the ways images, sounds, and other available cinematic modes interact to construct meaning. This article engages with research on embodiment, cognition, and multimodal artifacts to reveal how sound aids in the construction of film narratives by focusing on examples where sounds take the primary role in constructions of narrative meaning. Additionally, by discussing recent theories on cognition and multimodality, this article shows how sounds can evoke conceptual and narrative information in ways that stabilize our understanding of cinematic representations through the joint contribution of all of the available modes.

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 categoriesScience and technology studies, Insufficient 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: none
Teacher disagreement score0.876
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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0130.000

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 head0.097
GPT teacher head0.288
Teacher spread0.191 · 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