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Record W3022127275 · doi:10.3389/fbuil.2020.00058

Impact of Office Modernization on Environmental Satisfaction: A Naturalistic Field Study

2020· article· en· W3022127275 on OpenAlexaff
Amy Kim, Shuoqi Wang, Lindsay J. McCunn, Hessam Sadatsafavi

Bibliographic record

VenueFrontiers in Built Environment · 2020
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsWorkspaceArchitectural engineeringOpen planProcess (computing)EngineeringComputer scienceCivil engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

A case study was undertaken on one floor of a multi-floor office building in Seattle, WA. Its aim was to offer a straight-forward example for facilities managers, administrators, and researchers alike wishing to perform systematic, naturalistic, mixed-methods research in office spaces that have recently been retrofitted. Changes were made to the floor’s layout, and to the size of employees’ workspaces. New sound-making technology and a modern lighting framework were added. Objective measurements of lighting, acoustics, and indoor air quality were taken and an online questionnaire was distributed to staff to afford subjective measurements of their perceptions about the previous and new open-plan settings. Items concerning satisfaction with workspace layout, size, lighting, acoustics, air quality, and level of input into the retrofit process were asked. After the new space had been used for 1.5 months, occupants reported being more satisfied, in general, than they recalled being in the original setting. The size of personal workspaces and a sense of privacy were especially important to employees. Despite overhead lighting illuminance levels being below recommended industry standards, occupants were not dissatisfied with light levels. The sound masking system was iteratively commissioned based on negative occupant feedback, resulting in purposely setting some areas to exceed or fall short of acoustical performance guidelines; indoor air quality remained unchanged. Differences in quantitative and qualitative findings highlight the importance of gathering self-reported information from occupants in several ways and exploring them carefully to better understand why environmental satisfaction (or dissatisfaction) exists. Employees’ sense of environmental control remained a prominent theme in the data, supporting existing studies in the field of environmental psychology. While perceptions of control did not improve after the retrofit, occupants’ responses about the level of input they had into the retrofit process correlated significantly and positively with their perceptions of environmental satisfaction after its completion. The nuanced findings from this case study’s customized approach to measuring objective environmental stimuli, along with occupants’ environmental perceptions, add to a growing body of literature merging social scientific methodologies with technical environmental assessments for practical use by decision-makers working to satisfy employee preferences.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.264
Teacher spread0.248 · 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

Citations9
Published2020
Admission routes1
Has abstractyes

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