Legitimaciones sociales de las políticas patrimoniales y museísticas
Bibliographic record
Abstract
Cap. 1. Proyectos patrimoniales y museisticos en las sociedades democraticas y capitalistas: entre la legitimacion formal y la vinculacion social. Inaki Arrieta Urtizberea. Cap. 2. Musees et patrimoine immateriel au Quebec : enjeux politiques et sociaux. Laurier Turgeon. Cap. 3. “El patrimonio pertenece a todos”. De la universalidad a la identidad, ?cual es el lugar de la participacion social? Victoria Quintero Moron. Cap. 4. La legitimacion social y politica de los museos: dos casos del estado de Oaxaca, Mexico. Teresa Morales Lersch y Cuauhtemoc Camarena Ocampo. Cap. 5. Reinterpretaciones de la mision social de los museos: politicas de la cultura en la red de museos de Loures, Portugal. Marta Anico. Cap. 6. La comunicacion de los museos y sus relaciones con las politicas culturales de las ciudades. Entre la repeticion de estrategias y la innovacion. Daniel Paul i Agusti. Cap. 7. El Patrimonio de la Guerra Civil como util de concienciacion social al amparo de la Ley de la Memoria Historica. Oscar Navajas Corral y Julian Gonzalez Fraile. Cap. 8. Politica y planificacion museistica, y participacion social en Cataluna: un breve recorrido historico y algunas reflexiones. Daniel Sole i Llados. Cap. 9. Diagnostico de las acciones de los museos catalanes como parte de las politicas de integracion. Fabien Van Geert. Cap. 10. Los inexistentes alcornocalenos y las experiencias museisticas etnograficas en el Parque Natural Los Alcornocales. Agustin Coca Perez.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 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".