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Physical Symbols at Work: Communication of Cooperative Norms Through Table Shape

2013· article· en· W2039769834 on OpenAlexaff
Julian House

Bibliographic record

VenueAcademy of Management Proceedings · 2013
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOmnipresencePerceptionInterpersonal communicationSocial psychologyPsychologyTable (database)Symbolic interactionismWork (physics)Cognitive psychologyInterpersonal relationshipEpistemologyComputer science

Abstract

fetched live from OpenAlex

Despite its omnipresence in organizational life, the physical work environment has received relatively little scholarly attention or theoretical development, leading to a host of inconsistent findings and a recognition that as an important contextual variable it remains understudied, especially in comparison to the progress of other social sciences. This paper contributes to the development of one of the most promising theoretical perspectives on the physical work environment by being among the first to report evidence of the causal effect that mundane objects’ symbolic meanings can have on perceptions and behavior. Specifically, we examine the symbolic potential of tables of various shapes because of their central role in so many interpersonal interactions within and between organizations. In two experimental studies, we show that round tables increase perceptions of an organizations’ cooperative climate, and that groups seated at round tables cooperate more, relative to square and rectangular tables.

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)0.002
metaresearch head score (Gemma)0.022
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 score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.043
GPT teacher head0.318
Teacher spread0.275 · 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
Published2013
Admission routes1
Has abstractyes

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