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Record W4220660986 · doi:10.18432/ari29627

Inclinations

2022· article· en· W4220660986 on OpenAlexaffvenue
Danielle Peers, Alice Sheppard, Lindsay Eales, Abbie Schenk

Bibliographic record

VenueArt/Research International A Transdisciplinary Journal · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDanceNarrativeSociologyDisability studiesGenerativityDisabled peopleReferentPower (physics)GazeEconomic JusticeAbleismAestheticsWork (physics)PsychologyGender studiesSocial psychologyVisual artsPolitical scienceArtLinguisticsPsychoanalysisLaw

Abstract

fetched live from OpenAlex

Inclinations is an audio-described, 7-minute, site-specific, disability dance on film followed by a video discussion between three of the disabled artist-researchers. Throughout this project, we embraced a research-creation methodology to engage with the research question: How can we fully embed intersectional disability justice, not only as a theoretical lens, but also, as a methodological imperative? This work centres the concepts of disability culture and disability generativity, and purposefully diverges from more popular traditions of physically-integrated dance in favour of disability dance. We demonstrate and discuss how this choice—alongside various filmic practices—seeks to decentre the ableist gaze, normalizing narratives, and the ubiquitous non-disabled referent. Other methodological considerations enacted and discussed in this work include centring access aesthetics, consent, care, disability justice principles, and questions of power in every aspect of the creation process.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.951
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0050.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0490.011

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 head0.169
GPT teacher head0.520
Teacher spread0.351 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes2
Has abstractyes

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