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Record W2161295496 · doi:10.4309/jgi.2007.20.8

Recall of electronic gaming machine signs: A static versus a dynamic mode of presentation

2007· article· en· W2161295496 on OpenAlexvenueno aff
Sally Monaghan, Alex Blaszczynski

Bibliographic record

VenueJournal of Gambling Issues · 2007
Typearticle
Languageen
FieldPsychology
TopicSafety Warnings and Signage
Canadian institutionsnot available
Fundersnot available
KeywordsScrollingRecallCued recallMode (computer interface)HarmFree recallCued speechPsychologyComputer scienceAdvertisingAudiologyCognitive psychologySocial psychologyMedicineHuman–computer interactionArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

This study compared differences in rates of free and cued recall for messages displayed on electronic gaming machines (EGMs) delivered in one of two display modes: static or dynamic. Rates of recall were investigated in a laboratory setting using 92 university students (75.0% female) with a mean age of 19.3 years (SD = 2.4 years). The static mode consisted of a fixed government-mandated message placed on the frame of an EGM directly next to the gaming buttons. In the dynamic mode, an identical message was presented in the form of a translucent display scrolling across the screen during play. Results showed that significantly more of the information presented in dynamic mode was recalled, and with greater accuracy, in both free recall and cued recall conditions compared with static government-mandated messages. It was concluded that the method of displaying signs influences awareness and recall of harm minimization messages.

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)0.019
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.076
GPT teacher head0.440
Teacher spread0.364 · 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

Citations49
Published2007
Admission routes1
Has abstractyes

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