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Understanding Teachers' Perspectives on Field Trips: Discovering Common Ground in Three Countries

2006· article· en· W1982062231 on OpenAlexafffundabout
David P. Anderson, James Kisiel, Martin Storksdieck

Bibliographic record

VenueCurator The Museum Journal · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicMuseums and Cultural Heritage
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsTRIPS architectureField (mathematics)Field tripPublic relationsPolitical scienceValue (mathematics)Face (sociological concept)PedagogyMathematics educationSociologyGeographyPsychologyEngineeringSocial scienceTransport engineeringComputer scienceLaw

Abstract

fetched live from OpenAlex

The school field trip constitutes an important demographic market for museums. Field trips enlist the energies of teachers and students, schools and museums, and ought to be used to the best of their potential. There is evidence from the literature and from practitioners that museums often struggle to understand the needs of teachers, who make the key decisions in field trip planning and implementation. Museum personnel ponder how to design their programs to serve educational and pedagogical needs most effectively, and how to market the value of their institutions to teachers. This paper describes the overlapping outcomes of three recent studies that investigated teacher perspectives on field trips in the United States, Canada, and Germany. The results attest to the universality of some of the issues teachers face, and suggest improvements in the relationship between museums and schools.

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)0.008
metaresearch head score (Gemma)0.017
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 score0.091
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0100.011
Scholarly communication0.0080.007
Open science0.0010.008
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0020.000

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 head0.090
GPT teacher head0.256
Teacher spread0.166 · 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

Citations187
Published2006
Admission routes3
Has abstractyes

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