Bibliographic record
Abstract
Languages are certainly alive and well at museums when you talk out what you see, when you read the signs, when you chat with a friend, when you plan a visit! We open this issue on museums with some words from Matteo Casoni who shares some facts about our Swiss museums – do speakers of one national language have access to their language in a museum in another region of Switzerland…or not? With Floriana Sciumbata we travel to Trieste where hopefully those of us not so fluent in Italian can read the texts at the Museum of Literature. Elaine Gold takes us to Canada where we are brought a little closer to the languages of the First People and Mario Bottinelli Montandon delves into the slightly controversial world of English as a lingua franca in creating cultural landscapes. Camille Vorger se penche sur les processus d’acculturation et le rôle des émotions suscités par une exposition de peinture. Marie Le Mounier et Alexandra Gerber-Keller interrogent le rôle des images pour la construction de la pensée et de la parole. Vanessa Foscoli présente un projet muséal toscan faisant intervenir le récit de soi comme moteur de réflexion interculturelle et d’inclusion. Niles Stanley présente une intervention ambitieuse qui, par l’art et la poésie, favorise l’engagement des jeunes impliqués dans la communauté et montre clairement le lien entre la citoyenneté et le côtoiement des arts par le biais de l’empowerment social dans les espaces muséaux. Ilaria Compagnoni montre comment les collections artistiques mises en ligne par les musées et intégrées dans l’enseignement des langues peuvent favoriser des interdépendances positives entre les élèves. Denis Weger, Theresa Bogensperger, Margareta Strasser et Maria Zauner take us to Salzburg where historical sites come to life for German-as-a-foreign-language learners. With Julie Dorner et Larissa Bochsler we switch to French and go to Geneva where we experience elements from the “Parlez Vous MEG” project at the Musée d’Ethnographie Chiara Bartolini shows how audio guides are exploited to enhance receptive and productive skills, while Fabiana Fazzi investigates a project encouraging students to produce their own multimodal tours of the city of Venice as an open-air large-scale museum. Sabrina Bertollo presents a didactic scenario where picture description enhances oral skills. Ana Dal Negro introduces us to a tandem learning project for students of Italian at the Zurich Museum of Art and Alessandra Keller-Gerber shares how museum guides are trained to mediate not only between the exhibition and the public but also between languages. We are lucky to have Tom Reed contribute to making this issue beautiful! This issue takes you on a tour around the world and provides you with insights for making your language learning come alive by using the cultural and linguistic capital a local physical or exotic virtual museum can provide!
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.177 | 0.028 |
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".