Bibliographic record
Abstract
Il y a aussi des chapitres en francais. There are also chapters in English. Cap. 1. El complicado arte de exponer. Inaki Arrieta Urtizberea. Cap. 2. “Esta exposicion no es para este museo”. Las salas permanentes del Museu Valencia d’Etnologia. Joan Segui. Cap. 3. Debunking, Decentralizing and Dissonance: Cultural Jamming @ Museum of Vancouver. Viviane Gosselin. Cap. 4. L’exposition des objets de cultures autochtones aujourd’hui, gain ou perte de sens? Le cas de l’exposition « C’est notre histoire... » au Musee de la civilisation de Quebec. Daniel Arsenault et Nadine Desbiens. Cap. 5. El Born de Barcelona: exposiciones conmemorativas, limites, problemas y desafios. Francesc Xavier Hernandez Cardona. Cap. 6. Silencios y omisiones: narrando y exhibiendo la historia nacional. Magdalena Mieri. Cap. 7. Exhibiting the Commons. The Case of Tensta konsthall. Haizea Barcenilla Garcia. Cap. 8. Interactividad y patrimonio. Retos, tendencias y lineas de futuro. Nuria Serrat Antoli.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".