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Record W4378216428 · doi:10.1177/14687976231171713

Listening otherwise: From “silent tourism” soundscapes to privileged sonic ways of knowing

2023· article· en· W4378216428 on OpenAlexaff
Susan Frohlick, Celeste Macevicius

Bibliographic record

VenueTourist Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicHumor Studies and Applications
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsSoundscapeSilenceSociologyActive listeningReflexivityTourismPrivilege (computing)AestheticsMedia studiesSound (geography)HistoryCommunicationAcousticsArtPolitical science

Abstract

fetched live from OpenAlex

This paper explores quests for silence in tourism in English-language online media. We offer a reading of media forms all articulating a soundscape of silence: a preferred sonic environment for tourists’ health and well-being. A relational and reflexive approach allows us to interrogate these taken-for-granted desired silences, and to emplace ourselves in the analysis. Using critical discourse analysis, we try to listen to the sounds produced in the texts, and to write with, and about, the sounds that resonate with us, to disrupt the naturalization implicit to the silent tourism soundscapes. Such calls for silent tourism use vocabularies that reproduce a universalistic aurality and binaries of wanted/unwanted, silence/noise, nonhuman/human, good/bad that moralize and objectify. Relational ontology and feminist critiques of “soundscape” help us to rethink silent tourism as ways of knowing. Ultimately, in the media we reflect on and are not separate from sonic ways of knowing seem very much entangled with wealth, privilege, individualism, and settler positionalities.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.098
GPT teacher head0.376
Teacher spread0.277 · 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.

Study designNot applicable
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

Citations10
Published2023
Admission routes1
Has abstractyes

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