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Record W4362591137 · doi:10.29173/cgs150

Interpassive Gambling

2023· article· en· W4362591137 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueCritical Gambling Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsConcordia University
FundersSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et CultureConcordia University
KeywordsNoticeConsumption (sociology)Representation (politics)Computer scienceAdvertisingInternet privacyWorld Wide WebSociologyHuman–computer interactionBusinessPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

Slot machines are recognized as a particularly risky form of gambling. However, there is a form of slot machine consumption that seems to have largely escaped the notice of regulators and scholars: the streaming of slot machine play on YouTube and other platforms. In this article, we present the results of our qualitative study of 21 slot machine videos. Our study examines how these videos portray gambling and how they align with the norms of YouTube’s platform economy. Our analysis underscores the representation of slot machine gambling in this under-regulated media, emphasizing different tactics of viewer manipulation. We introduce the concept of interpassive gambling to reflect the ways that user-generated videos are a form of diffusion of gambling mechanics beyond traditional gambling venues. We conclude by calling for more scholarly and regulatory attention to this gamblified site of media consumption.

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.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.402
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.005

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 head0.348
GPT teacher head0.546
Teacher spread0.198 · 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