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Record W1989333090 · doi:10.1080/1357650x.2012.746349

Lateral bias in theatre-seat choice

2013· article· en· W1989333090 on OpenAlexaff
Victoria Harms, Miriam Reese, Lorin Elias

Bibliographic record

VenueLaterality Asymmetries of Body Brain and Cognition · 2013
Typearticle
Languageen
FieldEngineering
TopicSpatial Cognition and Navigation
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsContext (archaeology)PsychologyChartSocial psychologySelection (genetic algorithm)AdvertisingCognitive psychologyComputer scienceMathematicsArtificial intelligenceStatisticsGeographyBusiness

Abstract

fetched live from OpenAlex

Examples of behavioural asymmetries are common in the range of human behaviour; even when faced with a symmetrical environment people demonstrate reliable asymmetries in behaviours like gesturing, cradling, and even seating. One such asymmetry is the observation that participants tend to choose seats to the right of the screen when asked to select their preferred seating location in a movie theatre. However, these results are based on seat selection using a seating chart rather than examining real seat choice behaviour in the theatre context. This study investigated the real-world seating patterns of theatre patrons during actual film screenings. Analysis of bias scores calculated using photographs of theatre patrons revealed a significant bias to choose seats on the right side of the theatre. These findings are consistent with the prior research in the area and confirm that the seating bias observed when seats are selected from a chart accurately reflects real-world seating behaviour.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.237
Teacher spread0.219 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

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