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Record W2061656059 · doi:10.1080/13811118.2013.776459

An Investigation of the Motivations Driving the Online Representation of Self-Injury: A Thematic Analysis

2013· article· en· W2061656059 on OpenAlexaff
Karen Rodham, Jeff Gavin, Stephen P. Lewis, Jill M. St Denis, Peter K. Bandalli

Bibliographic record

VenueArchives of Suicide Research · 2013
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsThematic analysisWitnessPsychologyConfessionalRepresentation (politics)Social psychologyMythologyInternet privacySociologyQualitative researchPolitical scienceComputer sciencePoliticsHistory

Abstract

fetched live from OpenAlex

The objetive of the study was to identify a) the motivations for communicating about non-suicidal self-injury (NSSI) in a publicly accessible online forum, b) The significance (if any) of the "publicness" of the behavior. Using a Thematic Analysis of 423 text-based posts from an online NSSI forum, 5 motivations for using the site were identified: confessional, marking a turning point, acting as a deterrent, dispelling myths and offering or seeking support. Motivations for using the site differ markedly from motivations for engaging in NSSI and tend to be more outwardly focused. The publicness of the site therefore seems to be significant in terms of bearing witness, providing the opportunity to confront negative stereotypes, and the ability to seek and offer support to like-minded individuals.

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)0.014
metaresearch head score (Gemma)0.025
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 score0.014
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0040.003
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0010.001
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 head0.098
GPT teacher head0.419
Teacher spread0.321 · 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

Citations45
Published2013
Admission routes1
Has abstractyes

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