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Record W3183107114 · doi:10.1177/08862605211030015

Discourses Around Male IPV Related Systemic Biases on Reddit

2021· article· en· W3183107114 on OpenAlexaff
Marudan Sivagurunathan, David M. Walton, Tara Packham, Richard Booth, Joy C. MacDermid

Bibliographic record

VenueJournal of Interpersonal Violence · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsSt Joseph's Health CentreMcMaster UniversityWestern University
Fundersnot available
KeywordsThematic analysisDomestic violenceSocial mediaSilencePsychologyPoison controlHuman factors and ergonomicsGovernment (linguistics)Suicide preventionSocial psychologyMedicineQualitative researchPolitical scienceMedical emergencySociologySocial science

Abstract

fetched live from OpenAlex

To date research on intimate partner violence (IPV) has focused on the experience of females. The limited studies on male IPV survivors have shown that they are less likely to disclose their IPV experiences. Systemic biases may marginalize and silence male IPV survivors.The current study sought to explore the discourse around perceived systemic biases that may be present for male IPV survivors.A widely used social networking site (http://www.reddit.com/) was scraped for submissions relating to male IPV. Search was carried out using three keywords resulting in 917 submissions, out of which 82 met inclusion criteria. Submissions were included in final analysis if they consisted of more than half a page of data pertaining to male IPV. Thematic content analysis was utilized to analyze the data.Responses reflect common experiences with participants identifying multiple sources of perceived systemic biases: (1) social norms, (2) legal system, (3) social services, (4) media, and (5) government.The sources of potential support for male IPV survivors exhibit substantial pervasive biases against males as victims of IPV. Findings from current study can inform policies across multiple systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.794

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.337
Teacher spread0.306 · 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 teacher head, 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
Published2021
Admission routes1
Has abstractyes

Explore more

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