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Record W4237211427 · doi:10.31235/osf.io/embg4

Motivations of Cosplayers to Participate in the Anime Fandom

2018· preprint· en· W4237211427 on OpenAlexaff
Stephen Reysen, Courtney N. Plante, Sharon E. Roberts, Kathleen C. Gerbasi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMacEwan University
Fundersnot available
KeywordsFandomAnimeBelongingnessEntertainmentPsychologyLaundrySocial psychologyEveryday lifeSociologyMedia studiesArtGeographyPolitical science

Abstract

fetched live from OpenAlex

We examined differences between cosplaying and non-cosplaying anime fans with regard to their motivation to participate in the anime fandom. Participants, all anime fans, completed scales assessing a myriad of possible motivations for anime fandom participation. Cosplayers rated all of the assessed motivations higher than non-cosplayers. The highest-rated motivations for cosplayers included entertainment, escape from everyday life, belongingness, eustress, and aesthetic beauty. Modest sex differences were also found, as women were more likely than men to cosplay and, even among cosplayers, women reported higher belongingness, family, self-esteem, and escape motivations. With the exception of sexual attraction, however, where men were considerably more motivated by sexual attraction than women, the effect sizes for sex differences were fairly small, suggesting little true difference between male and female cosplayers. The results are discussed in relation to past research examining anime cosplayers.

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 score0.274
Threshold uncertainty score0.910

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 science0.0010.000
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 head0.067
GPT teacher head0.391
Teacher spread0.324 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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