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Record W3201217058 · doi:10.1016/j.metip.2021.100079

Picturing femininities and masculinities: Using visual methods to explore gender relations

2021· article· en· W3201217058 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMethods in Psychology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPhotovoiceVisual methodsThematic analysisPhoto elicitationVisual researchQualitative researchInclusion (mineral)Process (computing)PsychologySociologyComputer scienceSocial scienceVisual artsKnowledge managementCognitive scienceArt

Abstract

fetched live from OpenAlex

Arts-informed and arts-based methods are becoming more visible in qualitative psychological research. This study demonstrates how the use of visual images, through two visual methods photo-elicitation (PE) and photovoice (PV), can act as an innovative research tool for researchers. This paper focuses on the impacts of the visual images on the research process. A systematic search strategy was used to search 10 health and social science databases, with 2478 relevant articles retrieved, 197 articles were identified for review, and 75 articles met inclusion criteria. Qualitative synthesis and thematic analysis were selected to provide a flexible framework for addressing the research questions. The findings demonstrate the value of visual images being their ability to materialize bodies, social practices, and interactions between individuals and structures. These findings show that these two visual methods present researchers with an innovative process that can generate new insights and perspectives that are useful for studying aspects of gender relations (e.g., femininities and masculinities) and other aspects of social experience.

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.

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)0.017
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.437
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.869
GPT teacher head0.771
Teacher spread0.098 · 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