MétaCan
Menu
Back to cohort
Record W2033261654 · doi:10.1080/13811118.2013.805642

Responses to Online Photographs of Non-Suicidal Self-Injury: A Thematic Analysis

2013· article· en· W2033261654 on OpenAlex
Thomas G. Baker, Stephen P. Lewis

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

VenueArchives of Suicide Research · 2013
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLonelinessThematic analysisPsychologyPerceptionPoison controlSuicide preventionHuman factors and ergonomicsInjury preventionContent analysisThe InternetOccupational safety and healthApplied psychologySocial psychologyClinical psychologyMedicineQualitative researchMedical emergencyComputer science

Abstract

fetched live from OpenAlex

There is concern that graphic pictures of non-suicidal self-injury (NSSI) may detrimentally impact vulnerable viewers--namely those who may self-injure. How individuals (most who have, but some of whom have not, self-injured) respond to photographs of NSSI is currently unknown. Thematic analysis was used to assess testimony regarding NSSI imagery online. Analysis of testimony regarding NSSI photographs revealed a dichotomy. Individuals reporting positive perceptions said the photographs reduced loneliness and NSSI enactment. People reporting negative perceptions argued photographs reinforce and encourage NSSI. Experiences of being triggered by NSSI images were described by several participants. Photographs of NSSI posted online may have several risks for viewers. It is important to achieve a greater understanding of the effects of various forms of online NSSI content and to develop supportive NSSI resources on the Internet.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.425
Teacher spread0.352 · 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