Exhumaciones en el ámbito legal / Exhumations within Legal Realms. Panel 2 del congreso "Cuerpos incómodos: Violencia masiva, fosas comunes y necropolítica"
Bibliographic record
Abstract
Vídeo del Panel 2 del “Congreso Internacional. Cuerpos incómodos: Violencia masiva, fosas comunes y necropolítica”. Celebrado en Donostia en el marco de los cursos de verano de la Universidad del País Vasco los días 18-21 de julio de 2008. El panel 2 incluye las siguientes conferencias: Laura Pego (moderadora); Francisco Etxeberria (UPV-EHU/Aranzadi): El papel de Aranzadi en las exhumaciones de fosas de la Guerra Civil / The Role of Aranzadi in the Exhumations of Graves of the Civil War. Sévane Garibian (U Genève): Ley y restos humanos. En busca de un estatus jurídico / Law and Human Remains: In Search of a Legal Status. Stephanie Golob (CUNY-Baruch, NY): Inside Out: Transnational Legal Learning and Mass Grave Exhumation / De la fosa al mundo. El aprendizaje legal transnacional de las exhumaciones. Derek Congram (U Toronto): ¿“Mientras tanto, hasta cuándo? Medidas inmediatas para la recuperación de víctimas del conflicto en Colombia” / “In the Meanwhile”, Until When?: Immediate Measures for the Recovery of the Victims of the Colombian Conflict.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.007 |
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".