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Record W2046270763 · doi:10.1080/09650792.2013.872574

Re-imagining roles: using collaborative and creative research methodologies to explore girls’ perspectives on gender, citizenship and schooling

2014· article· en· W2046270763 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEducational Action Research · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsInstitute for Christian StudiesUniversity of Toronto
Fundersnot available
KeywordsPhotovoiceCitizenshipGirlSociologyGender studiesReputationPoliticsRhetoricPedagogyPsychologySocial sciencePolitical scienceDevelopmental psychologyVisual arts

Abstract

fetched live from OpenAlex

Often girls are the objects of the camera’s lens and subjects of the researcher’s gaze. This article describes a qualitative study using a collaborative, creative and critical methodology to explore girls’ perspectives on gender, citizenship and schooling. The seven adolescent girl participants used Photovoice to engage with their roles as girl-citizens. Despite Canada’s international reputation as a leader in gender equality, evidence reveals that challenges still prevent their full participation, particularly in formal politics. This research raises questions about what life is like for girls and women in Canada beyond the rhetoric and global rankings, and adds to the often disconnected bodies of literature on girlhood, citizenship and education.

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.

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: Empirical
About the Canadian research system: no · About a Canadian topic: no
Qualitativelow
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)0.042
metaresearch head score (Gemma)0.115
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 score0.198
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0420.115
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.002
Scholarly communication0.0010.000
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 head0.948
GPT teacher head0.767
Teacher spread0.180 · 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