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Record W4230602793 · doi:10.31234/osf.io/w8e4b

Exposure to High-Rise Buildings Negatively Influences Affect: Evidence from Real World and 360-degree Video

2020· preprint· en· W4230602793 on OpenAlexaff
Robin Mazumder, Hugo J. Spiers, Colin G. Ellard

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAffect (linguistics)High riseDegree (music)Built environmentPsychologyApplied psychologyEngineeringCivil engineeringCommunication

Abstract

fetched live from OpenAlex

Cities are densifying at a rapid rate, and accordingly, are constructing high-rise buildings to accommodate more people. The aim of this study was to quantify the physiological and psychological impacts of being in the presence of high-rise buildings in Central London, in a real and virtual 360-degree video environment. Using a within-subjects design, participants were exposed to a low-rise and high-rise building. While exposed, participants were monitored for electrodermal activity. They were also administered the Self-Assessment Manikin measure and a cognitive appraisal questionnaire. Participants rated the high-rise building environment to be less open, less friendly and rated themselves to feel less happy and have less sense of control, as compared to low-rise buildings. We found these effects in both the real world (n = 16) and a 360-degree video setting (n = 121). These findings suggest that city environments populated with high-rise buildings can have negative impacts on urban dwellers. Furthermore, this study provides a methodology to examine how individuals respond to the built environment and stands to inform urban design and architectural practices.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.005
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.120
GPT teacher head0.413
Teacher spread0.293 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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