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Record W2981985911

The Language of Suicide

2019· article· en· W2981985911 on OpenAlexaff
Victoria D. Cobuz

Bibliographic record

VenueStudent Research Proceedings · 2019
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsPhrasePsychologySuicide preventionSuicide attemptMental healthPoison controlPsychiatrySocial psychologyCriminologyMedicineMedical emergencyLinguistics
DOInot available

Abstract

fetched live from OpenAlex

There is a significant concern amongst caregivers, mental health advocates, and survivors of suicide attempts surrounding use of the phrase committed suicide. Sommer-Rotenberg (1998) identified that the phrase has a connotation of criminality, dishonor, and immorality, and that its ongoing use contributes to stigma surrounding suicide. Similar arguments have been made by others (see, e.g., Suicide.org; Suicideinfo.ca). In the current study, participants read two scenarios (in counterbalanced order), one depicting a suicide in which bereaved family members verbalize that they view the suicide as sinful and morally condemnable, and one depicting a suicide without this additional information. Analyses tested whether a suicide depicted as sinful is more frequently paired with statements employing the phrase committed suicide (relative to statements containing the phrase died by suicide) than the control scenario. Repeated measures ANOVA revealed that perceived sinfulness led to participants choosing the phrase committed suicide more than the phrase died by suicide. This study provides an empirical basis for a causal link between moral condemnation and the perceived appropriateness of the phrase committed suicide.   Faculty Mentor: Andrew Howell Department: Psychology

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)0.002
metaresearch head score (Gemma)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.106
GPT teacher head0.478
Teacher spread0.372 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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