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Record W4223995901 · doi:10.3390/su14084559

Festivals and Events as Everyday Life in Montreal’s Entertainment District

2022· article· en· W4223995901 on OpenAlexafffundabout
Edda Bild, Daniel Steele, Catherine Guastavino

Bibliographic record

VenueSustainability · 2022
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsMcGill UniversityCentre for Interdisciplinary Research in Music Media and Technology
FundersSocial Sciences and Humanities Research Council of CanadaMitacsMcGill University
KeywordsEntertainmentVitalityFraming (construction)Everyday lifeDiversity (politics)SoundscapeExpansiveSociologyGeographyPsychologyMedia studiesAdvertisingPolitical scienceVisual artsArtBusiness

Abstract

fetched live from OpenAlex

Cities struggle to balance vitality and livability, and noise is at the center of many of these debates. Preconceived ideas on the sonic expectations and needs of groups of city users can be misleading, particularly in entertainment districts such as the Quartier des Spectacles in Montreal (CA). We investigated what life was like in QDS for its year-round users during the 2019 festival season (the last before the COVID-19 pandemic), building on insights from residents, workers and visitors collected through online surveys. Respondents described an overall positive view of their district marked by a diversity of experiences and frustrations, with subtle intragroup differences between residents and workers. Age was an important variable framing these experiences, but unexpectedly, older respondents enjoyed their life in QDS just as much as younger users. Dissatisfaction with residing or working in QDS was rarely geared toward the frequency or loudness of festivals, but rather to other everyday life situations. Emergent from the data, we argued for the development of soundscape personas to refer to typologies of users whose experiences differ in terms of sonic priorities and evaluations. Our findings could inform strategies for organizing large events in urban areas, maintaining an awareness of diversity of 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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.363
Teacher spread0.350 · 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 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
Published2022
Admission routes3
Has abstractyes

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