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Self-Injury Behaviors in Cyber Space

2012· book-chapter· en· W2187717658 on OpenAlexaff
Jamie M. Duggan, Janis Whitlock

Bibliographic record

VenueIGI Global eBooks · 2012
Typebook-chapter
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsThe InternetIntervention (counseling)Variety (cybernetics)Internet privacyPsychologySpace (punctuation)Applied psychologyComputer scienceWorld Wide WebPsychiatry

Abstract

fetched live from OpenAlex

This entry describes the evolution and current state of research related to Non-suicidal self-injury on the Internet across a variety of mediums, including social networking websites, video-sharing websites, and informational websites. Although the full influence of such online behaviours on off-line behaviours and functioning remains relatively unknown, it appears that such activities pose both risks and benefits to mental health and wellbeing. Online activities may provide individuals with a history of or interest in self-injury with guidance and education, informal support, a sense of community, as well as allow for personal expression. However, such activities can also serve to trigger, reinforce, and normalize self-injury and may substitute for off-line relationships. The pervasiveness of self-injury online suggests that regular assessment of on-line activity is an important aspect of self-injury treatment. The chapter concludes with suggestions for utilizing the Internet as a novel approach to self-injury prevention and intervention efforts.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0200.003

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 head0.026
GPT teacher head0.305
Teacher spread0.279 · 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 designObservational
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

Citations5
Published2012
Admission routes1
Has abstractyes

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