The School Teacher and the Art Museum: A Multi- Case Study of Online Canadian Art Museum Teacher Resources | L’enseignant scolaire et le musée des beaux-arts : une étude de cas multiples des ressources en ligne des musées des beaux-arts pour les enseignants
Bibliographic record
Abstract
Abstract: This study seeks to better understand the online resources and lesson contents that Canadian art museums offer secondary school art teachers. The author conducted a content analysis of online teacher resources and lessons developed by four Canadian art museums during 2016. By looking at the various resources through the lens of a high school teacher/researcher, the author highlighted how these resources presented differences in curriculum and fostered self-reflection in students. The relationship between the art museum and the school teacher was examined. To better understand this relationship, further research on online resources developed by museums to increase pedagogical possibilities should be conducted.Key Words: Teacher; Museum; Online Teaching Resources; Art EducationRésumé : Cette étude vise une meilleure compréhension du contenu des leçons et des ressources en ligne mises à la disposition des éducateurs artistiques du secondaire par les musées des beaux-arts canadiens. L’auteur a analysé le contenu des ressources et des leçons en ligne offertes aux enseignants en 2016 par quatre musées des beaux-arts canadiens. En examinant les diverses ressources du point de vue d’un enseignant/chercheur au secondaire, l’auteur met en évidence les différences curriculaires de ces ressources et l’autoréflexion suscitée par ces ressources chez les étudiants. La relation entre le musée des beaux-arts et l’enseignant scolaire y est également étudiée. Il faudrait, pour mieux comprendre cette relation, mener d’autres recherches sur les ressources en ligne développées par les musées dans le but d’élargir l’éventail pédagogique.Mots-clés : enseignant; musée; ressources en ligne; éducation artistique
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".