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Record W40204019

Art and the museum : the educational partnership between a museum and a school

2006· dissertation· en· W40204019 on OpenAlexaboutno aff
Laurie Kader

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2006
Typedissertation
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipVisual arts educationThe artsPedagogySociologyVisual artsArtPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This thesis provides an inside look at the dynamics of museum/school partnerships. One of the more successful of these programs is Arrimage, a program which combines art and academic subject matter geared to enhance problem solving skills, cognitive development, and to sensitize children to art and its history. My research centers on the Arrimage partnership (musée d'art contemporain de Montreal), and examines the key components of positive, interactive relationships with art educators in museums and schools. I conducted three in-depth interviews with key, but diverse Arrimage participants, each conducted in their respective institutions; one museum school educator, one generalist school-teacher and one art specialist. The generalist school teacher and art specialist worked together in the same school. Each participant was strategically chosen for the purpose of gaining as much knowledge as possible about museum/school partnerships. The three interviews were transcribed and analyzed for a different and/or a similar working knowledge of the partnership. My research shows that when schools and museums partner their resources, students benefit from art education with increased artistic skills and self-awareness; learning through the arts can shape the future of education. A successful museum/school partnership is a comprehensive and creative way for students to have a hands-on and inspiring experience in art education.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.603
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.282
Teacher spread0.243 · 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 teacher head, not a consensus.

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

Citations1
Published2006
Admission routes1
Has abstractyes

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