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Record W4411994092 · doi:10.15353/cjds.v12i1.970

Cripping Digital Storytelling: Disability, Accessibility, and Celebrating Difference

2023· article· en· W4411994092 on OpenAlexafffundvenue
Kathleen C. Sitter, Brooke Allemang, Mica Pabia, Emma Gaunt, Ana Isabel González Herrera, Bruce Howell

Bibliographic record

VenueCanadian Journal of Disability Studies · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsUniversity of Calgary
FundersCanada Research Chairs
KeywordsStorytellingDigital storytellingDisability studiesSociologyGender studiesArtNarrativeLiteraturePedagogy

Abstract

fetched live from OpenAlex

The authors report findings from an 11-month research study where disabled adults created digital stories about life-stage transitions related to employment, post-secondary, and community living. While results about transitions have been published elsewhere, this article focuses on the online digital storytelling process as experienced by disabled adults. It begins with a brief description of crip theory and its relationship to disability. Next is an overview of the research design: a two-phased process where 13 post-secondary disabled youth were trained as digital storytelling peer-facilitators, and subsequently co-facilitated a series of digital story workshops over 8 months involving 34 disabled participants. Peer-facilitators and participants completed pre- and post-workshop surveys. Responses were analyzed guided by crip theory and content analysis. The second half of the article reports on survey findings which indicated several themes: disability pride, centering disability perspectives, the importance of crip time, and the need to consider community connection. It ends with a discussion and considerations in designing and replicating accessible online digital storytelling workshops to remove ableist barriers, amplify community, and ultimately celebrate difference.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.157
GPT teacher head0.422
Teacher spread0.265 · 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

Citations5
Published2023
Admission routes3
Has abstractyes

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