Toward Evaluating Art Museum Education at the Art Gallery of Ontario
Bibliographic record
Abstract
Abstract: Over the past three decades, the museum education field has seen a rise in the frequency of program evaluation. In this paper, I convey little known information about program evaluation at the Art Gallery of Ontario by presenting my findings from an interview I conducted with Judy Koke, the gallery’s Chief of Public Programming and Learning. Our discussion highlights both the barriers the AGO has faced on their journey toward evaluating programmatic value and the strategies the gallery has employed in an effort to enhance its internal evaluation efforts. A brief overview of program evaluation in museums provides the background to this discussion. KEYWORDS: Art museum education; Program evaluationRésumé: Le domaine de la pédagogie muséale a connu au cours des trois dernières décennies un essor quant au nombre d’évaluations de programmes. Je transmets ici le peu de renseignements connus sur l’évaluation des programmes au Musée des beaux-arts de l’Ontario (AGO), au terme d’une entrevue que j’ai menée avec Judy Koke, directrice de l’apprentissage et de la programmation à l’intention du public au Musée. Notre discussion met en évidence tant les obstacles rencontrés par l’AGO dans le cadre de l’évaluation de la valeur des programmes que les stratégies utilisées par le musée pour rehausser ses activités internes d’évaluation. Un bref aperçu de l’évaluation des programmes dans les musées met notre discussion en contexte.MOTS CLES: Éducation musée d'art; évaluation du programme.
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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 | 0.024 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".