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Record W4416112502 · doi:10.1177/16094069251394113

Out to See: A Journey to Critical Feminist Polyethnography

2025· article· en· W4416112502 on OpenAlexaff
Lisa Trefzger Clarke

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsTrent University
Fundersnot available
KeywordsTransformative learningAutoethnographyReflexivityPraxisFeminismFeminist philosophyCritical theoryAccountabilityFeminist epistemologyMentorship

Abstract

fetched live from OpenAlex

This methodological insight into critical feminist polyethnography demonstrates an expansion of critical feminist autoethnography to include queer and decolonizing theories, and reflexive praxis for social justice and transformative research with an interconnected culture group. Through critical feminist polyethnography, the researcher demonstrates a responsive and consent-based relationship with the culture group of study participants, feminist counsellors and therapists, as an insider/outsider to the research topic. A visual model of the methodology, inspired by the nautilus, assisted the participants and the researcher to engage with each theoretical chamber, ensuring consent and accountability were breathed into each step of the research. The research was reflective, responsive, and iterative, exploring how feminist counsellors and therapists engaged in learning about feminism, applied a feminist lens to psychotherapeutic modalities, and experienced peer and clinical mentorship and supervision. Using critical feminist polyethnography, the researcher was able to make ontological connections between the participant’s stories of feminist learning and transformative learning theoretical revisiting. Using a visual model as a pedagogical tool, the researcher and participants engaged with the research questions, transcriptions, and recommendations for emergent, transformative action.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models agreeAgreement compares identical category sets and study designs across arms.

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)0.053
metaresearch head score (Gemma)0.114
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.672
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0530.114
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.945
GPT teacher head0.844
Teacher spread0.101 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical · Methods

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
Published2025
Admission routes1
Has abstractyes

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