Bibliographic record
Abstract
Este art?culo argumenta que los procesos de memoria hist?rica necesitan ser con textualizados con respecto a otros movimientos relacionados con los derechos hu manos en la Espa?a del siglo XXI. Se cuestiona aqu? la representaci?n de la me moria hist?rica como un fen?meno aislado y la contextualiza como uno de varios procesos representativos d? la maduraci?n de la democracia espa?ola que se vio a partir de 1995. El punto de partida del art?culo es la producci?n cultural y el acti vismo sociolegal que se ha visto alrededor de la violencia de g?nero. Los textos cul turales analizados en t?rminos de la evoluci?n de ideas y leyes en contra de la vio lencia de g?nero y la trata de mujeres incluyen: Alg?n amor que no mate (1996), Solas (1999), Te doy mis ojos (2003), En la puta vida (2001), Princesas (200$), Salvajes (2001), Poniente (2002) y Volver (2006). Al final las autoras ponen de relieve la poca atenci?n que se ha prestado a la violencia de g?nero en compa raci?n con la memoria hist?rica y proponen una reconsideraci?n global de todos los cambios que han venido a definir la Espa?a del siglo XXI como un estado progresista y comprometido a un futuro ?tico.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".