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Record W2779217352 · doi:10.1080/02601370.2017.1406543

The transformative-learning potential of feminist-inspired guided art gallery visits for people  diagnosed with mental illness and addiction

2017· article· en· W2779217352 on OpenAlexaffabout
Lauren Spring, Melissa J. Smith, Maureen DaSilva

Bibliographic record

VenueInternational Journal of Lifelong Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicAdult and Continuing Education Topics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsTransformative learningFraming (construction)Consciousness raisingFeminismCritical consciousnessSociologyMental illnessPsychologyMental healthPedagogyGender studiesPsychotherapist

Abstract

fetched live from OpenAlex

Consciousness-raising practices at the heart of feminism remain one of the most vital components of transformative learning theory and provide the foundation for its constructivist underpinnings. Recently, there has been a call for educators to employ consciousness-raising practices outside of traditional classroom settings and to focus greater attention on the ‘extra-rational’ aspects of education – especially when working with marginalised adult learners. This is very much in keeping with engaged feminist pedagogy that allows space for emotions in learning. Reflecting on our experiences facilitating access to art programmes with people diagnosed with mental illness and addiction at the Art Gallery of Ontario, we highlight several examples of gender-related consciousness-raising that emerged. These experiences suggest that exploring mental illness through engagement with art in a social setting allows participants to deepen their understanding of art and the political implications of their life experiences. These tours also seem to serve as a powerful counterpoint to more clinical and masculine ways of framing mental health and well-being. Ultimately we argue that it is our explicitly feminist approach to transformative learning and not the existence of accessibility programmes themselves that holds the promise of redefining what and who museums are for.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.673

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.328
Teacher spread0.317 · 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 designObservational
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

Citations11
Published2017
Admission routes2
Has abstractyes

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