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Record W4390099982 · doi:10.1525/jsah.2023.82.4.449

Too Much Information: Noise and Communication in an Open Office

2023· article· en· W4390099982 on OpenAlexaff
Joseph Clarke

Bibliographic record

VenueJournal of the Society of Architectural Historians · 2023
Typearticle
Languageen
FieldEngineering
TopicArchitecture, Modernity, and Design
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDistractionNoise (video)Software deploymentQUIETTelecommunicationsArchitectureGermanTraffic noiseSpace (punctuation)Movement (music)Computer scienceSociologyComputer securityAcousticsHistoryPsychologyArchaeologyNoise reductionArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Noise was among the most contested issues in the large open offices that proliferated after World War II in Europe and North America. The “landscape” offices that developed out of the German Bürolandschaft movement were known for large floor plates filled with misaligned desks. They were meant to improve employees’ communication, but their acoustic design prompted worker anxieties about distraction and diminishing privacy. While early remediation efforts sought to quiet offices, in the 1960s designers began adding random, unintelligible noise to mask distractions and arranging employees according to their expected sound levels. This shift from eliminating noise to embracing it as a space-defining element reflected a powerful new acoustic paradigm. The Bürolandschaft movement waned in the 1970s, but the judicious spatial deployment of noise remains an invaluable technique as designers consider how architecture can help or hinder communication and collective intellectual activity.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0080.018
Scholarly communication0.0150.010
Open science0.0010.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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