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Record W2496929103 · doi:10.1177/1461444816660783

The self—harmed, visualized, and reblogged: Remaking of self-injury narratives on Tumblr

2016· article· en· W2496929103 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.

Bibliographic record

VenueNew Media & Society · 2016
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsAffordanceNarrativeSocial mediaContext (archaeology)IconographyFace (sociological concept)Content (measure theory)SociologyAestheticsMedia studiesPsychologyLiteratureVisual artsComputer scienceArtHistoryCognitive psychologySocial science

Abstract

fetched live from OpenAlex

Images featuring self-injury (SI) have been proliferating on social media. This article reports the findings of a visual narrative analysis of 294 photo-based posts on Tumblr, exploring how SI is narrated through the interplay between image content, photographic composition, associated texts and tags, and reblogging. Findings reveal a shift in the iconography of SI from direct depictions of self-injured bodies to re-appropriations of popular media content that figuratively represent emotional struggles. Images of self-inflicted wounds received 10 times less reblogs than images without wounds, and media memes conveying hopeless moods were the most widely distributed. These memes represent SI as a form of life struggle virtually anyone can face while complicating conventional readings of SI as an individual pathologic experience. We discuss these findings in the context of an emergent online curation culture and how Tumblr’s unique affordances may both offer and limit possibilities for narrating SI.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.512

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.031
GPT teacher head0.347
Teacher spread0.316 · 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