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Record W4382653191 · doi:10.35844/001c.77373

Transforming Masculinities Through Cross-Cultural Collaboration: Reflections on Building a Community of Practice Framework

2023· article· en· W4382653191 on OpenAlexaffabout
Liza Lorenzetti, Aamir Jamal, Rita Dhungel, Gabrielle Jamela Hosein, Sarah Thomas, Jeffery Halovorsen

Bibliographic record

VenueJournal of Participatory Research Methods · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsUniversity of the Fraser ValleyUniversity of Calgary
Fundersnot available
KeywordsTransformative learningIntersectionalityEquity (law)Public relationsSociologyPoliticsPolitical scienceWork (physics)Gender studiesPedagogyEngineering

Abstract

fetched live from OpenAlex

Interdisciplinary, local, regional, and cross-regional efforts are required to further men’s gender justice engagement on a global scale. There is limited understanding of how cross-regional collaborations account for intersectionality, geo-political differences among stakeholders, and the value of local strategies when designing and sharing prevention frameworks. Catalyzed by emerging and long-standing gender equity movements, our interdisciplinary research team from Canada, the Caribbean, Nepal, and Pakistan employed a community of practice (CoP) framework to share and mobilize research and experiential knowledge with the purpose of promoting regional and cross-regional strategies to involve men in gender justice efforts. Through a collective process, we co-created position statements, process dimensions, and key CoP activities to root our international collaboration. In this article, we emphasize the unique local contexts for our work and the learnings that emerged from our CoP. We propose a framework that can be used to advance collective and interdisciplinary agendas across global contexts and further the work of groups committed to transformative social change.

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)0.051
metaresearch head score (Gemma)0.034
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0510.034
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0030.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.774
GPT teacher head0.733
Teacher spread0.041 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2023
Admission routes2
Has abstractyes

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