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Record W4396832826 · doi:10.1145/3613904.3641940

Remembering through Sound: Co-creating Sound-based Mementos together with People with Blindness

2024· article· en· W4396832826 on OpenAlexaff
MinYoung Yoo, William Odom, Arne Berger, Samuel Barnett, Sadhbh Kenny, Priscilla Lo, Samien Shamsher, Gillian Russell, Lauren Knight

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsSound (geography)ReminiscenceBlindnessSound designPsychologyComputer scienceCognitive psychologyAcoustics

Abstract

fetched live from OpenAlex

Sound is a preferred and dominant medium that people with blindness use to capture, share and reflect on meaningful moments in their lives. Within the timeframe of 12 months, we worked with seven people with blindness and two of their sighted loved ones to engage in a multi-stage co-creative design process involving multiple steps building toward the final co-design workshop. We report three types of sonic mementos, designed together with the participants, that Encapsulate, Augment and Re-imagine personal audio recordings into more interesting and meaningful sonic memories. Building on these sonic mementos, we critically reflect and describe insights into designing sound that supports personal and social experiences of reminiscence for people with blindness through sound. We propose design opportunities to promote collective remembering between people with blindness and their sighted loved ones and design recommendations for remembering through 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 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)0.005
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0060.005
Open science0.0010.008
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.307
Teacher spread0.283 · 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 designQualitative
Domainnot available
GenreEmpirical

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

Citations14
Published2024
Admission routes1
Has abstractyes

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