MétaCan
Menu
Back to cohort
Record W4247429289 · doi:10.1121/1.4800502

Investigating soundscape affordances through activity appropriateness

2013· article· en· W4247429289 on OpenAlexaboutno aff
Frederik L. Nielbo, Daniel Steele, Catherine Guastavino

Bibliographic record

VenueProceedings of meetings on acoustics · 2013
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSoundscapeAffordanceContext (archaeology)Noise (video)Human–computer interactionPsychologyObject (grammar)Computer scienceCognitive psychologyApplied psychologyArtificial intelligenceSound (geography)AcousticsGeography

Abstract

fetched live from OpenAlex

Central to the concept of soundscape is the understanding of the acoustic environment in context. Previous research indicates that people understand soundscapes through their potential for activities. One way to look at activities is through the concept of affordances - defined as the actionable properties of an object. In this study, the object is a location and time in the city. Fifteen participants listened to stereo recordings of 8 outdoor sites in Paris and Montreal. In each trial, they evaluated on a continuous scale how appropriate the soundscapes were for a given activity. Four activities were considered and presented in random order: studying for an exam, meeting up with a friend, riding a bike and relaxing. Participants justified their ratings in free-format comments. A 8(Soundscapes) x 4(Activities) factorial ANOVAs revealed significant effects of Soundscape and Activities and Soundscape*Activities on appropriateness ratings. Certain soundscapes were found to accommodate specific activities only while others were found to potentially accommodate all activities or none (prominent mechanical/traffic noise.) We also analyzed comments to further understand how participants envision utilizing the soundscape/environment and attribute meanings to the various sounds present.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0000.000

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 head0.039
GPT teacher head0.344
Teacher spread0.306 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations29
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of meetings on acousticsSame topicNoise Effects and ManagementFrench-language works237,207