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Record W4393316041 · doi:10.1177/15327086241234705

Misfits Meet Art and Technology: Cripping Transmethodologies

2024· article· en· W4393316041 on OpenAlexafffund
Carla Rice, Eliza Chandler, Fady Shanouda, Chelsea Temple Jones, Ingrid Mündel

Bibliographic record

VenueCulture Studies &#x2194 Critical Methodologies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsCarleton UniversityToronto Metropolitan UniversityBrock UniversityUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPraxisNormativeSociologyProcess (computing)AestheticsPoliticsComputer scienceEpistemologyArtPolitical sciencePhilosophyLaw

Abstract

fetched live from OpenAlex

This article thinks with disability theory and artistic praxis to explore how disabled artists repurpose and invent technologies in artistic processes designed to enact care and access, extend embodiment, satiate the senses, and create crip culture. Drawing on four examples, we claim that disabled artists are creative technologists whose non-normative culture-making practices approach accessibility as a transmethodological process that requires and generates new forms of interconnected technology and artfulness. Disabled artists, as “creative users,” change the uses and outcomes of technology, dis-using technologies in ways that lead to a more dynamic understanding of access and with it, of crip cultures as processual, artful, and political.

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)0.015
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.018
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0150.138
Scholarly communication0.0180.020
Open science0.0020.018
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0070.001

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 head0.249
GPT teacher head0.517
Teacher spread0.269 · 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 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

Citations11
Published2024
Admission routes2
Has abstractyes

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