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Record W4312811867 · doi:10.1177/1071181322661050

Reconcile Voice Communication and Auditory Distraction in Military Open-Plan Workspace Design

2022· article· en· W4312811867 on OpenAlexaff
Wenbi Wang, Angela Yee‐Moon Wang, Ann Nakashima

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2022
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsDistractionWorkspaceComputer sciencePlan (archaeology)Adjacency listWorkstationHuman–computer interactionSimulationRobotPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

Facilitating voice communication and reducing auditory distraction are common requirements in layout design of military open-plan workspaces. The implications of these requirements on between-operator adjacency however are often contradictory. To support the design of such work environments, a new analytical procedure was developed to evaluate layout options according to their impact on both requirements. This procedure was explained in a modeling study to compare two frequently used layout configurations, i.e., outward-facing versus inward-facing workstation setups. The results predicted a quieter vocal effort for the speakers and a lower risk of auditory distraction for all listeners in the outward-facing configuration. The study demonstrated the usefulness of this analytical procedure to support layout design of military workplaces where a balanced consideration of communication and distraction is required.

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)0.004
metaresearch head score (Gemma)0.019
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 score0.004
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.000
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 head0.034
GPT teacher head0.264
Teacher spread0.230 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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