Rethinking Docent Training at the Montreal Museum of Fine Arts: A Pilot Project
Bibliographic record
Abstract
Abstract: In 2014-2015, the Montreal Museum of Fine Arts (MMFA) and Concordia University's School of Extended Learning conducted a joint pilot project with the objective of determining the effectiveness of a new approach for training volunteer museum guides based on a dialogic method. In this paper, we address: (i) the conceptualization of alternative training based on dialogic concepts; (ii) the development of a new course outline; (iii) the trial period during which volunteers took part in test training; (iv) and, finally, aspects of what we learned during this undertaking. This pilot project is important because it will influence future docent training at the MMFA and possibly elsewhere.KEYWORDS: Museum education; docent training; dialogic teaching; video elicitation; participant-centered exchangesRésumé: Le Musée des beaux-arts de Montréal (MBAM) et l’École d’apprentissage prolongé de l’Université Concordia ont mené en 2014-2015 un projet pilote concerté afin de déterminer l’efficacité d’une nouvelle approche de formation des guides bénévoles au musée, approche fondée sur une méthode dialogique. L’article couvre les points suivants : (i) la conceptualisation d’une nouvelle formation fondée sur les concepts dialogiques ; (ii) la mise au point d’un nouveau schéma de cours ; (iii) la période d’essai pendant laquelle les bénévoles ont suivi la nouvelle formation à l’étude ; et finalement (iv) les différents aspects de ce que nous avons appris au cours de cette démarche. Ce projet pilote est important car il influencera la formation future des guides-interprètes du MBAM et peut-être même ailleurs.MOTS CLES: Éducation muséale; formation guide bénévole; enseignment dialogique; incitation par video; les échanges participant
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".