MétaCan
Menu
Back to cohort

Competing Agendas: Young Children's Museum Field Trips

2008· article· en· W2060668522 on OpenAlexaff
David P. Anderson, Barbara Piscitelli, Michele C. Everett

Bibliographic record

VenueCurator The Museum Journal · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTRIPS architectureVisitor patternCompetition (biology)Variety (cybernetics)Field (mathematics)Order (exchange)Museum educationSociologyPolitical sciencePublic relationsPedagogyEngineeringEcologyBusinessComputer science

Abstract

fetched live from OpenAlex

Abstract Visitors to museum settings have agendas that encompass a wide variety of missions. Agendas are known to directly influence visitor behavior and learning. Numerous agendas are at play during a visit to a museum. We suggest that in a museum‐based learning experience, children's agendas are often overlooked, and are at times in competition with the accompanying adult's agendas. This paper describes and qualitatively analyzes three episodes of competing agendas that occurred on young children's field trips to museums in Brisbane, Australia. The aim is to elucidate the kinds of tensions over agendas that can arise in the experience of young museum‐goers. Additionally, we hope to alert museum practitioners to the importance of considering children's agendas, with the aim of improving their museum experience. Suggestions are also made for ways in which educators can address children's agendas during museum visits in order to maximize learning outcomes.

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)0.007
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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0070.004
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.043
GPT teacher head0.218
Teacher spread0.176 · 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

Citations36
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueCurator The Museum JournalSame topicMuseums and Cultural HeritageFrench-language works237,207