Aprehendiendo al delincuente: crimen y medios en América del Norte
Bibliographic record
Abstract
Presentación; Introducción; I. El crimen narrado; Peligrosidad delito y justicia en el México posrevolucionario / Saydi Núñez Cetina; El que ríe al último ríe mejor: “mujercitos” en la nota roja durante los años setenta en México / Susana Vargas Cervantes; Nota roja and journaux jaunes: Popular Crime Periodicals In Quebec and Mexico Will Straw; II. El crimen filmado; Flores del mal en el cine mexicano. La devoradora criminal o el tipo clásico moderno / Álvaro A. Fernández; Crime/Scene: Reanimating the Femme Fatale in David Lynch’s Hollywood Trilogy / Alanna Thain; Hiding in Plain Sight: Masquerading Genre In David Cronenberg’s A History of Violence / Bart Beaty; Sin City la representación de la violencia / Víctor Manuel Granados Garnica; Nadie sabe para quién trabaja: el crimen transfronterizo según la Canadian Broadcasting Corporation / Graciela Martínez-Zalce; III. El cotidiano criminalizado; The Godfather Is Dead: A Hybrid Model of Organized Crime / Natasha Tusikov; Aproximaciones al consumo de drogas en Canadá. Comunidades provincias; North American Digital Copyright Regional Governance And the Persistence of Variation / Blayne Haggart; Sobre los autores
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".