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Record W4317862946 · doi:10.32920/21950894

Self-Injury in Japanese Manga: A Content Analysis

2023· preprint· en· W4317862946 on OpenAlexaff
Yukari Seko, Minako Kikuchi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDistressPsychologyContent analysisGazeContent (measure theory)PerceptionGirlSocial psychologyDevelopmental psychologyClinical psychologyPsychoanalysisSociology

Abstract

fetched live from OpenAlex

This study explored representations of self-injury in Japanese manga (graphic novels). A content analysis of fifteen slice-of-life manga published between 2000-2017 was conducted, focusing on forty scenes that depict eighteen characters engaging in self-injury. Most depictions of self-injury reflect a stereotypical perception of “self-injurer,” a young girl cutting herself to cope with negative emotion. Characters receive informal support from friends and partners, while parents are portrayed as unsupportive and even triggering. An emergent trend was observed among manga targeting male readers to label selfinjuring women as “menhera” - mentally vulnerable damsels in distress - inviting a fetishistic gaze on the self-injuring female bodies.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0020.002
Scholarly communication0.0020.001
Open science0.0000.002
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 head0.117
GPT teacher head0.367
Teacher spread0.250 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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