MétaCan
Menu
Back to cohort
Record W2016647803 · doi:10.1177/0013916505283419

The Physical and Psychological Measurement of Gambling Environments

2006· article· en· W2016647803 on OpenAlexaff
Karen Finlay, Vinay Kanetkar, Jane Londerville, Harvey H. C. Marmurek

Bibliographic record

VenueEnvironment and Behavior · 2006
Typearticle
Languageen
FieldPsychology
TopicColor perception and design
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPleasureLegibilitySet (abstract data type)PsychologyContrast (vision)Environmental psychologyFocus (optics)Design elements and principlesSocial psychologyApplied psychologyAdvertisingComputer sciencePsychotherapistArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

This research examined the influence of the physical design of gambling venues on emotion. Two competing casino designs were identified. According to Kranes's playground model, casinos should include environmental elements designed to induce pleasure, legibility, and restoration. In contrast, Friedman proposed a set of design principles focusing on the machines as the dominant feature of the décor. Three exemplars of each design were identified. Measures of emotional reactions to the casinos were collected from 22 people who had gambled in all six casinos. Kranes-type casinos yielded significantly higher ratings than did Friedman-type casinos on pleasure and restoration (relief from environmental stress). Future research should focus on design variations that can be built into a Friedman-type setting to enhance restoration.

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)0.005
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.105
GPT teacher head0.334
Teacher spread0.229 · 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

Citations39
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueEnvironment and BehaviorSame topicColor perception and designFrench-language works237,207