The self—harmed, visualized, and reblogged: Remaking of self-injury narratives on Tumblr
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".