Bibliographic record
Abstract
Il y a aussi des chapitres en français. There are also chapters in English. Cap. 1. Recordar y olvidar: emprendedores y lugares de memoria, Iñaki Arrieta Urtizberea. Cap. 2. De monumentos de piedra a patrimonio inmaterial. Estrategias políticas, museológicas y museográficas de presentación de la memoria. Xavier Roigé. Cap. 3. La représentation du conflit dans les musées en Irlande du Nord : stratégies d'exposition. Karine Bigand. Cap. 4. La memoria del exilio republicano a través de sus espacios: patrimonio, turismo y museos en el territorio catalán transfronterizo. Jordi Font Agulló, David González Vázquez, Gemma Domènech Casadevall y Salomó Marquès Sureda. Cap. 5. De las trincheras al museo: sobre el reciente proceso de patrimonialización de la Guerra Civil española en Euskadi. Xabier Herrero Acosta y Xurxo M. Ayán Vila. Cap. 6. Donner corps à une mémoire et faire éprouver le passé : appréhender la muséalisation de la mémoire au Musée de la Stasi de Berlin Lichtenberg. Marie Hocquet. Cap. 7. Le visible et l’invisible des mémoires douloureuses aux musées. Dominique Chevalier. Cap. 8. Patrimonialización de lugares vinculados a memorias traumáticas: políticas públicas sobre el pasado reciente en Uruguay. Ana María Sosa. Cap. 9. Del relato oficial a la recepción de los visitantes: análisis de la puesta en escena del pasado reciente en el Museo de la Memoria y de los Derechos Humanos de Chile. Malena Bastías Sekulovic. Cap. 10. Creating Peace at the Canadian War Museum. Kathryn Lyons. Cap. 11. Promenades sur les champs de ruines. Jean-Yves Boursier.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.118 | 0.025 |
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".