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Record W3046860260 · doi:10.7575/aiac.ijalel.v.9n.3p.1

An Investigation of Suicide Notes: An ESP Genre Analysis

2020· article· en· W3046860260 on OpenAlexaff
Atekah Abaalkhail

Bibliographic record

VenueInternational Journal of Applied Linguistics & English Literature · 2020
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsRhetorical questionSuicidologyPerspective (graphical)LinguisticsTest (biology)PsychologyDiscourse communityDiscourse analysisSociologySuicide preventionPoison controlArtMedicineVisual arts

Abstract

fetched live from OpenAlex

Suicide notes are considered important texts used to understand the suicidal act. Most studies focused on these notes psychologically to test hypothesis. Less research has been done discursively from the perspective of language studies. The purpose of this study is to investigate suicide notes, written by English speaking males and females between the years 1945-1954 and 1983-1984, from the perspective of English for Specific Purposes (ESP) genre approach. Specifically, the study examines the communicative purpose(s) and the rhetorical move/step structure in a corpus of 86 suicide notes. The findings suggest that suicide notes share common communicative purposes and rhetorical structure, and, therefore, constitute a genre from the ESP perspective. By examining the rhetorical move structure of suicide notes, this study proposes a model of suicide notes structure, the moves writers use and suggests that suicide notes do constitute a genre without a visible discourse community. The study adds to the existing body of knowledge in genre theory and makes a theoretically based contribution to the fields of genre studies, suicidology, and, potentially, forensic linguistics.

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)0.004
metaresearch head score (Gemma)0.012
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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0090.005
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.002
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 head0.024
GPT teacher head0.322
Teacher spread0.298 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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Same venueInternational Journal of Applied Linguistics & English LiteratureSame topicSuicide and Self-Harm StudiesFrench-language works237,207