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Record W1977347492 · doi:10.1080/09647770903073060

Museum experiences that change visitors

2009· article· en· W1977347492 on OpenAlexafffundabout
Barbara J. Soren

Bibliographic record

VenueMuseum Management and Curatorship · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of Toronto
FundersAga Khan Foundation Canada
KeywordsExhibitionTransformational leadershipTransformative learningMuseologySociologyVisual artsPublic relationsPolitical scienceArtPedagogy

Abstract

fetched live from OpenAlex

Transform, transforming, and transformative are common terms for describing museum spaces, the creation of objects on display, and experiences for visitors. But is there evidence that museums profoundly change visitors through their objects, collections, exhibitions, public programs, and websites? The nature of transformational museum experiences and potential ‘triggers for transformation’ are the focus of this article. Two case studies describe ways in which visitors articulate change they have experienced. Included are projects about teachers and artists during an intense two-week summer institute in an interdisciplinary museum and about visitors to a traveling exhibition highlighting the role Canada plays in international development. Individuals’ comments and questions indicated that experiences with authentic objects and the unexpected, highly emotional responses, new cultural and attitudinal understandings, as well as motivation to become more proactive in the way they live their lives, may have been triggers for transformational experiences.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0110.012
Scholarly communication0.0100.005
Open science0.0010.015
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0160.001

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 head0.091
GPT teacher head0.236
Teacher spread0.146 · 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 designQualitative
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

Citations123
Published2009
Admission routes3
Has abstractyes

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