La memoria en los márgenes: la literatura testimonial concentracionaria de Nora Strejilevich escrita desde el exilio
Bibliographic record
Abstract
El articulo se propone hacer un aporte al estudio de un tipo de escritura con frecuencia marginada y marginal: la literatura testimonial concentracionaria argentina, es decir, aquellos textos escritos por supervivientes de los campos de concentracion o centros de detencion clandestinos de la ultima dictadura militar argentina (1976-1983), en los que estos sujetos relatan su propia experiencia traumatica. El estudio parte de la idea de que las memorias de la violencia totalitaria en diversos contextos socio-culturales tienen una instancia significativa de construccion en el exilio y, por esta razon, ocupan un espacio marginal. Otra causa de dicha marginalidad se observa en el hecho de que una porcion importante de los discursos producidos por los testigos se definen como «testimonio», un genero que todavia presenta problemas de definicion y especificidad en el ambito de la critica literaria. Se analizaran dos textos de Nora Strejilevich, ex-secuestrada y exiliada en Canada y Estados Unidos: Una sola muerte numerosa (1997) y El arte de no olvidar (2006). Ya sea desde el testimonio, como en el primer caso, o desde la reflexion teorica sobre el mismo, como en el segundo, estos textos contribuiran a la discusion que se abre en torno a la literatura testimonial concentracionaria argentina de los ultimos anos.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".