Vers de nouveaux musées « hybrides »
Bibliographic record
Abstract
Table ronde réunissant Marie Chapman, directrice du Musée canadien de l’immigration (Halifax), Marcelo Huernos, chercheur au MUNTREF-Musée de l’immigration (Buenos Aires), Joanne Hyppolite, conservatrice de la diaspora africaine au Smithsonian National Museum of African American History and Culture (Washington D. C. Unis), Karen Moeskops, directrice du Red Star Line Museum (Anvers), Zineb Sedira, artiste, Sebastien Tyrakowski, directeur adjoint du Musée de l’émigration de Gdynia (Pologne), modérée par Emily Miller, directrice des apprentissages et des partenariats au Musée de la migration (Londres).Les musées de migrations font face au défi de la mise en récit des histoires qui ont façonné les pays et les cultures à travers les siècles. Les archives, les traces des trajectoires migratoires faites d’objets et de récits personnels, mais aussi le recours à l’art contemporain contribuent à rendre accessibles et à révéler ces histoires. L’interdisciplinarité et le croisement des regards sont au cœur des réflexions des musées de migrations. Tout concourt au décloisonnement et à la transversalité des disciplines dans un équilibre entre médiation et esthétique, entre les différents types d’œuvres et d’objets, la participation de la société civile, les choix scénographiques et pédagogiques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.035 | 0.004 |
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".