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Record W2478984194 · doi:10.3138/md.59.2.3

At the Edges of Meaning: Aging, Memory, and Resonant Listening

2016· article· en· W2478984194 on OpenAlexvenueno aff
Kim Sawchuk, A. C. Moorhouse

Bibliographic record

VenueModern Drama · 2016
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordsForgettingActive listeningNarrativeConversationMeaning (existential)PsychologyNeurotypicalReminiscenceCognitive psychologyLinguisticsAestheticsCommunicationArtLiteratureDevelopmental psychologyAutismPhilosophy

Abstract

fetched live from OpenAlex

This article explores the idea of resonant listening in relation to aging and memory loss. Resonance is put into conversation with three pieces of sound art created by or with older adults grappling with changes in memory. The first two works discussed, Sounds of Forgetting and Turning, were created from recordings of conversations between one of the authors, Aynsley Moorhouse, and her father, Dr. John Moorhouse. The third composition, Fried Brains, is a two-minute piece created by Louise Jacks and Enid Anderson, two older women. Shifting away from narratives that reduce aging and memory changes to a series of tragic losses from a golden time in one’s life, these three works assert the value of ongoing expressive capacity and collaboration. They point to the ways in which, through sound, we may gradually learn to destigmatize dementia and changes in memory to create connections between neurotypical and neurologically atypical people.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.017
Scholarly communication0.0050.006
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.041
GPT teacher head0.316
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2016
Admission routes1
Has abstractyes

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