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Record W4388946886 · doi:10.32920/24624984

Cripistemologies of Disability Arts and Culture: Reflections on the Cripping the Arts Symposium

2023· preprint· en· W4388946886 on OpenAlexafffund
Eliza Chandler, Katie Aubrecht, Esther Ignagni, Carla Rice

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of GuelphToronto Metropolitan UniversitySt. Francis Xavier University
FundersOntario Arts CouncilCanada Council for the Arts
KeywordsDisability studiesThe artsMedical model of disabilitySociologyGender studiesIntellectual disabilityAbleismDeaf cultureLearning disabilityInclusion (mineral)PsychologyPolitical scienceDevelopmental psychologyLawPsychiatryLinguistics

Abstract

fetched live from OpenAlex

<p>[Introduction:] "In 2014, in the introduction to their special issue of the Journal of Literary and Cultural Disability Studies, Merri Lisa Johnson and Robert McRuer introduced the field of disability studies to the concept of cripistemologies. As described by Johnson and McRuer, although there are many different ways of knowing disability that circulate throughout our culture, epistemologies of disability generated outside of disability experience, community, and activism are the most legible and lucrative within a neoliberal culture, and therefore most readily taken up (2014, p. 128). Disability studies, disability activism, and disability arts and culture as imbricated movements led by and for disabled people that advance a disability politic, centre meanings of disability that are generated through Deaf, disabled, and mad people’s experiences and knowledge. These ways of knowing disability are succinctly expressed through the term “cripistemology,” which refers to knowing, and not knowing, disability through disability experiences as these are understood by and for disability communities (2014, p. 127)."</p>

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.864
Threshold uncertainty score1.000

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 studies0.0010.003
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 head0.227
GPT teacher head0.435
Teacher spread0.208 · 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.

Study designTheoretical or conceptual
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
Published2023
Admission routes2
Has abstractyes

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