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Record W4247594979 · doi:10.32920/ryerson.14652639

Mass Shootings and Intimate Partner Violence: A Feminist Critical Discourse Analysis

2021· preprint· en· W4247594979 on OpenAlexaff
Leah Mallinos

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDomestic violenceIntimate partnerCritical discourse analysisCriminologyIntervention (counseling)Mass mediaSociologyGender studiesPolitical scienceSuicide preventionPoison controlPsychologyLawPoliticsIdeologyMedicine

Abstract

fetched live from OpenAlex

This Major Research Paper is a feminist critical discourse analysis of news articles pertaining to three mass shootings in the United States in which an intimate partner of the perpetrator was targeted and a history of domestic violence was known. The aim of this study is to identify and examine the dominant discourses employed by the media when reporting on mass shootings that are rooted in gender-based violence. I uncovered three discourses: 1) the continued portrayal of intimate partner violence as a private issue; 2) the emphasis on the shock and disbelief held by community members; and 3) the construction of perpetrators as deviant that is detached from IPV. The purpose of this study is that through naming and understanding these discourses at play, social workers can better engage in the critical prevention, intervention and postvention work of addressing gun violence and its inextricable link to intimate partner violence.

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)0.012
metaresearch head score (Gemma)0.011
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 score0.012
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.005
Science and technology studies0.0100.022
Scholarly communication0.0110.009
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.392
Teacher spread0.362 · 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
Published2021
Admission routes1
Has abstractyes

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