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Record W4224224070 · doi:10.1177/26349795221084144

Sonic stories, sensory ethnography, and listening with an injured mind

2022· article· en· W4224224070 on OpenAlex

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

VenueMultimodality & Society · 2022
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsYork University
Fundersnot available
KeywordsActive listeningEmbodied cognitionPsychologyEthnographySensory systemAestheticsCognitive scienceCognitive psychologyHistoryCommunicationArtEpistemologyPhilosophy

Abstract

fetched live from OpenAlex

Brain injuries transform how one’s world sounds. What follows are two sonic stories. These short audio compositions are designed to transport the listener into the pre- and post-brain injury sensory environment—a textured and embodied landscape that non-injured minded individuals, including most clinicians, have little understanding of. This lack of understanding is a consequence of the sorts of neurological research done in the scientific traditions which tend to leave certain forms of sensory phenomena unstudied and exclude patients’ voices. We draw inspiration from Rachel Kolb’s (2017) first-person account of hearing music for the first time after getting cochlear implants. She writes that music jolted her core in ways she could not explain. Instead of “Can you hear the music?”, she prefers to be asked, “What does music feel like to you?” Stemming from the perspectives of two individuals that live with brain injuries (identified here as Story A and Story B ), these sonic stories ask what does a brain injury sound like?

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.365
Teacher spread0.299 · 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