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Record W4391749709 · doi:10.26522/ssj.v18i1.3992

Portraits of Resistance: Exploring Intra-personal, Social, and Institutional Resistances through the Use of Arts-Based Research among Racialized Parents of Autistic Children and Youth

2024· article· en· W4391749709 on OpenAlexaffvenue
Fiona J. Moola, Nivatha Moothathamby, Stephanie Posa, Methuna Naganathan

Bibliographic record

VenueStudies in Social Justice · 2024
Typearticle
Languageen
FieldPsychology
TopicFamily and Disability Support Research
Canadian institutionsSunnybrook HospitalUniversity of TorontoToronto Metropolitan University
Fundersnot available
KeywordsPortraitThe artsResistance (ecology)PsychologySociologySocial psychologyDevelopmental psychologyVisual artsArt

Abstract

fetched live from OpenAlex

The lives of children who live at the intersectional nexus between childhood autism and race may be considered as “shadow stories” that have remained silenced in autism literature. We explored the experiences of racialized parents who provide care to autistic children. We drew on a theoretical framework known as DisCrit and decolonizing arts-based methodologies. Racialized parents of autistic children demonstrated resistance along various themes, including fighting the system, protecting my child, and creating cultural communities. We join black girlhood studies, critical race theory, and disabled children’s childhood studies by continuing the journey of decentering Whiteness in childhood disability research. We demonstrate how disabled racialized communities engage in activism and social justice while forming powerful counter-discourses.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0170.022
Scholarly communication0.0060.005
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.553
GPT teacher head0.511
Teacher spread0.041 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2024
Admission routes2
Has abstractyes

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