Art and the museum : the educational partnership between a museum and a school
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.033 | 0.017 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".