MétaCan
Menu
Back to cohort
Record W4392378937 · doi:10.1080/09540253.2024.2315052

Moving beyond masculine defensiveness and anxiety in the classroom: exploring gendered responses to sexual and gender based violence workshops in England and Ireland

2024· article· en· W4392378937 on OpenAlexaff
Debbie Ging, Jessica Ringrose, Betsy Milne, Tanya Horeck, Kaitlynn Mendes, Ricardo Castellini de la Silva

Bibliographic record

VenueGender and Education · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsWestern University
FundersArts and Humanities Research Council
KeywordsMasculinityIrishFraming (construction)Gender studiesEthosSexual violenceResistance (ecology)SociologyMeritocracySexual abuseHegemonic masculinityPsychologySocial psychologyPoison controlCriminologySuicide preventionPolitical science

Abstract

fetched live from OpenAlex

Increasing rates of gender-based and sexual abuse, coupled with a rise in misogynistic influencers online, have become a growing issue in UK and Irish schools. This paper reports on the findings of a postlockdown study in England and Ireland that piloted workshops on gender-based and sexual violence. While most student responses were positive, we found that roughly 10% of girls and 20% of boys were resistant. In this paper, we explore these critical responses, focusing specifically on male resistance. Our findings indicate that new strategies, which avoid the concept of ‘toxic masculinity’, are needed to help boys move from defensive to empathetic engagements. We also find that the neoliberal, meritocratic ethos of many schools has fostered a problematic framing of gender-based violence as genderneutral. We conclude that it is vital to adopt an intersectional, whole-school approach to educating about sexual violence, which acknowledges male victimhood, while also emphasizing gendered privileges.

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)0.005
metaresearch head score (Gemma)0.008
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 score0.031
Threshold uncertainty score0.062

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0080.008
Scholarly communication0.0070.003
Open science0.0020.007
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.001

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 head0.073
GPT teacher head0.326
Teacher spread0.253 · 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

Citations26
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueGender and EducationSame topicGender, Feminism, and MediaFrench-language works237,207