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Record W4206516048 · doi:10.31234/osf.io/ynbmr

Push Outcomes Bias Perceptions of Scratch Card Games

2021· preprint· en· W4206516048 on OpenAlexafffund
Alexander C. Walker, Madison Stange, Mike J. Dixon, Jonathan A. Fugelsang, Derek J. Koehler

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScratchLotteryFeelingPsychologyOutcome (game theory)PerceptionAdvertisingSocial psychologyComputer scienceBusinessEconomics

Abstract

fetched live from OpenAlex

In the domain of scratch card gambling, “pushes” refer to outcomes in which a prize is won that is equal to the cost of a scratch card game. Despite resulting in no net monetary gain, these outcomes are categorized as wins by lottery operators, effectively inflating published scratch card information (e.g., posted odds of winning). Additionally, the experience of obtaining a push shares similarities (e.g., the revealing of matching symbols) with the experience of obtaining a win and thus may be experienced similarly to wins by gamblers. Across four studies (N = 1502), we examined the impact of push outcomes on participants’ perceptions of scratch card games. In Studies 1 and 2, participants reported feeling more likely to win, more excitement to play, and a stronger urge to gamble when presented with a scratch card that categorized push outcomes as wins compared to when presented a scratch card that did not categorize these outcomes as wins. In Study 3, participants experiencing a push outcome prior to a loss reported feeling more likely to win compared to those not experiencing a push outcome yet experiencing the same net monetary loss. In Study 4, push outcomes were found to elicit more excitement and a stronger urge to gamble compared to losses but less excitement and a weaker urge to gamble compared to wins. Overall, the present investigation suggests that push outcomes, a prevalent feature of scratch card games, can bias gambling-related judgments and increase the appeal of scratch card games.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.002
Research integrity0.0000.001
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 head0.282
GPT teacher head0.454
Teacher spread0.172 · 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 teacher head, not a consensus.

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

Citations1
Published2021
Admission routes2
Has abstractyes

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