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Record W1503924539 · doi:10.1002/asi.23282

The design and formative evaluation of nonspeech auditory feedback for an information system

2015· article· en· W1503924539 on OpenAlexaff
Rafa Absar, Catherine Guastavino

Bibliographic record

VenueJournal of the Association for Information Science and Technology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicTactile and Sensory Interactions
Canadian institutionsMcGill UniversityUniversity of British Columbia
Fundersnot available
KeywordsComputer scienceFormative assessmentHuman–computer interactionAuditory feedbackAuditory displaySound (geography)MultimediaPsychologyAcoustics

Abstract

fetched live from OpenAlex

This research describes a user‐centered design method for creating nonspeech auditory feedback to enhance information interactions with a visual information system. It involves 2 studies. In the first, a user‐centered sound design method is used, based on one originally applied for visually impaired users. Three panels of end users are employed to collaboratively and iteratively design the required nonspeech sounds. The method ensures that the sounds designed are not based on designers' personal or ad hoc choices and instead exploits the creativity of a user group as an application of participatory sound design. Based on the results of this study, recommendations are made for extending the sound design method to novel interfaces and sighted users. A second study involves a formative evaluation of the information system integrated with the designed auditory feedback. This evaluation confirms that the user‐centered sound design method leads to the creation of auditory feedback which conveys meaningful information to users.

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)0.025
metaresearch head score (Gemma)0.059
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.059
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.329
Teacher spread0.257 · 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 designObservational
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

Citations3
Published2015
Admission routes1
Has abstractyes

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