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Record W4381429923 · doi:10.23860/jfs.2023.22.02

The Centrality of Community in Education about Gender-Based Violence

2023· article· en· W4381429923 on OpenAlexaboutno aff
Catherine Vanner

Bibliographic record

VenueJournal of Feminist Scholarship · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Women's Rights
Canadian institutionsnot available
Fundersnot available
KeywordsCentralityPsychologySociologyMathematicsStatistics

Abstract

fetched live from OpenAlex

The Time to Teach about Gender-Based Violence in Canada project asked teacher and student participants how Canadian educators could improve young people’s critical consciousness in relation to gender-based violence. Data collection involved individual interviews with 14 teachers, participatory workshops with three groups of students, and a virtual workshop in which teacher participants validated and expanded upon initial analysis of their interview data and responded to cellphilms produced in the student workshops. Drawing upon feminist and engaged pedagogy and situating gender-based violence as a form of difficult knowledge, analysis identifies community as a central concept for effective teaching about gender-based violence from both teacher and student perspectives. The concept of community is broken down into creating community, teaching in community, and connecting with communities. Teacher participants indicated that their capacity to create and sustain transformative learning communities would be enhanced by further support from the educational communities that they are members of.

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)0.007
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.535
Threshold uncertainty score0.936

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0260.034
Scholarly communication0.0090.005
Open science0.0010.011
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.092
GPT teacher head0.389
Teacher spread0.297 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2023
Admission routes1
Has abstractyes

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