Bibliographic record
Abstract
No es raro que el jurado que le otorgo a esta novela el Premio Memorial Silverio Canada, durante la celebre Semana Negra de Gijon, se fijara en la obra de Ernesto Mallo, porque esta narracion, Crimen en el barrio del once, reune generos y meritos suficientes como para no pasar desapercibida. De una parte, es una solvente novela negra, con personajes bien dibujados, y por encima de todos el del comisario Lascano, alias El Perro, y su amigo Fuseli, el medico forense que lo aprecia guardando las distancias; tambien es un relato politico, o historico, que al situar la accion en la epoca negra de la dictadura argentina, nos ofrece un panorama siniestro del pais y de la corrupcion y sadismo que lo controlaba a todos los niveles; y finalmente, Crimen en el barrio del once es una novela de amor, aunque sea de amor ultraterreno, pues Lascano vive amargado por la muerte en un coche de su mujer, Marisa, que le duro demasiado poco y en cualquier caso no lo suficiente como para convertirlo en una persona mejor que la persona que el quiere ser, o es capaz de ser porque, obviamente, si se ha entregado en cuerpo y alma a su profesion y a la impasibilidad de caracter, es como metodo de supervivencia. Lascano es un hombre que tiene que llevar una armadura porque por dentro esta roto.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".