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Record W3091777149 · doi:10.4309/jgi.2021.46.8

Pachinko/Pachislot Playing Participation in Japan: Results from a National Survey

2020· article· en· W3091777149 on OpenAlexvenueno aff
Akiyo Shoun, Akira Sakamoto, Yukiko Horiuchi, Kumiko Akiyama, Ishida Hitoshi, Kikunori Shinohara, Yasunobu Komoto, Taku Sato, Naoyuki Nishimura, Nobuo Makino

Bibliographic record

VenueJournal of Gambling Issues · 2020
Typearticle
Languageen
FieldPsychology
TopicGambling Behavior and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDemographyStratified samplingPsychologyGeographyHumanitiesMedicineSociologyArt

Abstract

fetched live from OpenAlex

To understand individuals' pachinko/pachislot playing behavior, one of the major games in Japan, we conducted a national study of Japanese residents between 18 and 79 years old. From resident records, in which all Japanese citizens are registered, 9,000 individuals were chosen by the two-stage stratified random sampling method. The number of individuals who submitted valid responses was 5,060 (response rate: 56.2%). The analysis result indicated that 582 (11.5%) played pachinko/pachislot in the last 12 months ("past-year players"). Compared to "non-players" (those who never played pachinko/pachislot before or did not play in the last 12 months), past-year players had higher 12-month participation rates in all 10 gambling activities other than pachinko/pachislot. To compare demographic variables between the past-year players and non-players, χ2 tests were carried out. The tests discovered that past-year players were more likely to be males in their 30s, junior high school graduates, and making a similar annual household income to the median value for all respondents. Next, demographic variables were compared for each participation level and significant differences between age groups were found; participants in their 60s and 70s visited pachinko/pachislot parlors more frequently than those in their 20s to 40s. This is the first study to reveal the details of pachinko/pachislot playing behavior in Japan.RésuméUne enquête nationale a été menée auprès de résidents japonais âgés de 18 à 79 ans dans le but d’étudier le comportement des joueurs de pachinko/pachislot, l’un des principaux jeux pratiqués au Japon. À partir du registre de déclaration de résidence, auquel tous les citoyens du pays sont inscrits, 9000 personnes ont été sélectionnées en suivant la méthode d’échantillonnage aléatoire stratifié en deux étapes. Sur ce nombre, 5060 ont donné des réponses valides (taux de réponse : 56,2 %). Selon les résultats, 582 (11,5 %) ont joué au pachinko/pachislot dans les 12 derniers mois (« joueurs de l’année précédente »). Comparativement aux « non-joueurs » (à savoir ceux qui n’ont jamais pratiqué ce jeu dans le passé ou au cours des 12 derniers mois), les joueurs de l’année précédente affichent sur 12 mois des taux de participation plus élevés à 10 autres activités de jeux de hasard outre le pachinko/pachislot. Des tests de chi-square ont été réalisés en vue de comparer les variables démographiques entre les joueurs de l’année précédente et les non-joueurs. Les premiers étaient plus susceptibles d’être des hommes dans la trentaine et des diplômés des écoles intermédiaires, dont le revenu familial annuel se rapprochait de la valeur médiane pour l’ensemble des répondants. Les variables démographiques ont été comparées pour chaque niveau de participation, faisant apparaître des différences notables entre les groupes d’âge : les sexagénaires et les septuagénaires fréquentaient les maisons de pachinko/pachislot plus souvent que les générations de la vingtaine à la quarantaine. Cette étude est la toute première à révéler des détails sur les habitudes de jeux de pachinko/pachislot au Japon.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.023
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.511
GPT teacher head0.502
Teacher spread0.009 · 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.

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

Citations5
Published2020
Admission routes1
Has abstractyes

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