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Record W4405214009 · doi:10.26522/ssj.v18i4.4363

Art, Heart, and Pedagogy for Social Change

2024· article· en· W4405214009 on OpenAlexaffvenue
Elizabeth Brulé, Katya F. Kredl, Juliette Vaillancourt, Elise Zhao

Bibliographic record

VenueStudies in Social Justice · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsMcGill UniversityQueen's University
Fundersnot available
KeywordsSocial changeSociologySocial pedagogyPolitical sciencePedagogyLaw

Abstract

fetched live from OpenAlex

This article is a collective discussion with undergraduate students about their work in a second-year gender studies course. The discussion shares how active engagement in collective art production for social change can provide the seeds for decolonial, anti-racist and anti-ableist pedagogical practice. The course encourages students to actively engage in the classroom, raise questions and concerns about social justice, and implement ways to challenge social relations of power. Students work collectively on projects using a range of alternative ways of knowing, including sensory, heart, intellectual, and spiritual knowledge, to connect with the course material in creative ways. The article is a conversation with three students in the course and the work they produced. They discuss the various mediums they used, including poetry, collaging, and a case study of artists’ street art. The students touch on the politics of joy, self-care, heart knowledge, politics of suffering, and accessibility, illustrating how combining art with various ways of knowing has helped them develop deeply analytic, compassionate, and relational work for social change. The work affirms ways of knowing that have often evolved outside the colonial academic institution. The anti-racist, anti-colonial, anti-ableist, feminist and Indigenous pedagogies used in the course help to pluralize constructive capacities for more decolonizing, equitable, inclusive, anti-racist and expansive educational futures.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.303
GPT teacher head0.427
Teacher spread0.124 · 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 designNot applicable
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

Citations1
Published2024
Admission routes2
Has abstractyes

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