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Record W4392165128 · doi:10.1111/imcb.12738

Access all areas: multisensory science exhibitions tailored toward blind, low‐vision and diverse‐needs communities

2024· article· en· W4392165128 on OpenAlexaff
Erica Tandori, Laura Ciacchi, Lisa Ciacchi, Jennifer D. Ly-Huynh, Stuart Favilla, Jamie Rossjohn

Bibliographic record

VenueImmunology and Cell Biology · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicConservation Techniques and Studies
Canadian institutionsInstitute of Infection and Immunity
FundersAustralian Research Council Centre of Excellence in Advanced Molecular ImagingRoyal SocietyDepartment of Industry, Science, Energy and Resources, Australian GovernmentMonash UniversityAustralian Research CouncilUniversity of New South WalesRetina Australia
KeywordsOutreachExhibitionCitizen scienceVision sciencePsychologyPublic relationsMedical educationSociologyPolitical scienceComputer scienceMedicineVisual artsArtificial intelligence

Abstract

fetched live from OpenAlex

Monash Sensory Science is a scientific outreach initiative specifically tailored to members of the community who are blind, have low vision and have diverse needs. The purpose of this initiative is to showcase Australian science and encourage greater participation in science from these often-overlooked communities. This article presents our experience in establishing Monash Sensory Science at Monash University and inspiring other institutions to launch similar outreach events.

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)0.002
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.131
Threshold uncertainty score0.438

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0070.001
Scholarly communication0.0020.002
Open science0.0010.010
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1310.018

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 head0.071
GPT teacher head0.308
Teacher spread0.237 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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