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Record W2946119747 · doi:10.3828/jlcds.2018.47

Blindness Simulation and the Culture of Sight

2019· article· en· W2946119747 on OpenAlexaff
Tanya Titchkosky, Devon Healey, Rod Michalko

Bibliographic record

VenueJournal of Literary & Cultural Disability Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Rights and Representation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSightBlindnessPower (physics)CuriosityRelation (database)PsychologyNatural (archaeology)CertaintySociologyPhenomenonAestheticsSocial psychologyEpistemologyComputer scienceHistoryArtPhilosophyMedicineOptometry

Abstract

fetched live from OpenAlex

“What’s it like?” This question has stimulated the simulation of disability through such activities as sitting in a wheelchair, putting in ear plugs, or putting on a blindfold. Disability simulation is a curious phenomenon, stimulated as it is by a curiosity that springs from the certainty that ability and disability are essentially opposite experiences. The article theorizes simulation in relation to blindness as it appears in educational awareness campaigns and fundraising initiatives, as well as in literary endeavors. Making use of cultural disability studies, the article reveals the disability imaginary at play in the culture of sight and its simulation exercises. The authors explicate the sense of knowledge production that imagines blindness as “not seeing” and sight as a “natural authority.” This follows a path where the difference between knowing and understanding is explored. Such a path neither debunks nor justifies blindness simulations as an educational power but instead aims to reveal sighted culture’s interest in simulation as a way of knowing.

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)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.061
Scholarly communication0.0120.006
Open science0.0010.010
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.420
Teacher spread0.364 · 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 designNot applicable
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

Citations21
Published2019
Admission routes1
Has abstractyes

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