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Record W4406393990 · doi:10.7202/1115469ar

Exploring Women’s Transformative Learning and Community Building through Practicing Martial Arts to Disrupt Gendered and Hetero-Patriarchal Norms

2024· article· en· W4406393990 on OpenAlexaff
Emily Dobrich

Bibliographic record

VenueInternational Journal for Talent Development and Creativity · 2024
Typearticle
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransformative learningMartial artsGender studiesSociologyThe artsVisual artsAestheticsPsychologyPedagogyArt

Abstract

fetched live from OpenAlex

This article explores the potential for martial arts to support transformation and community building for women. Findings indicate women can derive many individual benefits from learning martial arts. Yet, the benefits must extend beyond the individual level to create social change. Based on an evaluation of literature on women’s experiences learning martial arts, I use my perspective as an adult education researcher and a feminist lens to propose creative approaches to supporting women in learning martial arts. Supporting women in learning martial arts requires promoting creativity and invention in practice. Feminist new materialism, transformative learning theory, and communities of practice are the theories that guide the direction of this article. The major contribution of this article is to offer creative approaches for imagining a feminist praxis through martial arts that could foster learning environments that encourage self-determination and build social support and resistance to hetero-patriarchal power and gender inequity, which has relevance to broader educational settings and communities.

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)0.000
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 score0.367
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.169
GPT teacher head0.397
Teacher spread0.229 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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