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Record W4311627865 · doi:10.1177/14703572221111804

The optometry of visual communication: the Museum of Vision Science

2022· article· en· W4311627865 on OpenAlexaffabout
Jane Griffith

Bibliographic record

VenueVisual Communication · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsVision scienceConversationVisual artsSociologyHistory of sciencePoint (geometry)OptometryArtMedicineComputer scienceEpistemologyCommunicationArtificial intelligence

Abstract

fetched live from OpenAlex

This article uses the Museum of Vision Science and other museums of optometry as an entry point for considering the science of seeing and the seeing of science. The Museum of Vision Science, the only optometric museum in Canada, is part of an optometry school. The placement of the Museum of Vision Science within an optometry school perhaps harkens back to 19th-century examples of professional schools and museums, and this article suggests ways the museum could offer a dynamic approach to humanities understandings of vision within a science curriculum. But, more broadly, this article uses the case study of the Museum of Vision Science to consider larger possibilities for visual communication studies in conversation with optometry. Although the museum situates itself as one of vision science, it also offers a complicated ‘cultural’ history of vision. The author bridges questions of the hegemony of vision science with the practice of visual communication studies.

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)0.003
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: Other
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0140.032
Scholarly communication0.0140.010
Open science0.0010.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.028
GPT teacher head0.354
Teacher spread0.326 · 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
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

Citations0
Published2022
Admission routes2
Has abstractyes

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