L'expérience d'adolescents en visite scolaire dans un musée de beaux-arts : effets comparatifs des modalités de médiation interpersonnelle et numérique
Bibliographic record
Abstract
Cet article s’intéresse aux modalités de médiation interpersonnelle et numérique mises en œuvre au sein d’un musée de beaux-arts à l'intention du public adolescent. Une étude réalisée au Musée des beaux-arts de Montréal (MBAM) a permis de rendre compte de l’expérience muséale d’adolescents (n=35) ayant participé à une visite guidée par une médiatrice et une visite assistée par un parcours-jeu sur tablette numérique. À partir d’un modèle de la situation pédagogique et du concept d’engagement (comportemental, cognitif et émotionnel), les deux contextes de visites sont observés puis comparés. Les résultats sont ensuite présentés selon les deux modalités de médiation. Les données quantitatives permettent d’estimer la superficie visitée, le nombre d’œuvres vues et le temps d’observation, tandis que les données qualitatives montrent la perception et l’appréciation de ces deux expériences de visite par les participants. Comme les mêmes adolescents ont expérimenté les deux modalités de médiation, une approche comparative nous permet de rendre compte des différentes formes de leur engagement et de leurs préférences de visite. This article is interested in the modalities of interpersonal facilitation and digital implementations within a fine arts museum for teenage audiences. A study performed at the Montreal Museum of Fine Arts (MMFA) involved an experiment with teenagers (n=35) who participated in a visit guided by a facilitator, as well as in a visit accompanied by a route-discovery game on digital tablets. Starting from a pedagogical model and from the notion of engagement (behavioural, cognitive and emotional), the two visit contexts are observed and then compared. The results are then presented according to the two facilitation modalities. The quantitative data allows for an estimation of the total area visited, the number of works visited, and observation time, while the qualitative data illustrates the participants’ perception and appreciation of these two visit experiences. As the same teenagers experienced the two facilitation modalities, a comparative approach enables us to report on the different forms of their engagement and their visit preferences.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".