Exploring Cultural Criminology
Bibliographic record
Abstract
Explorando a criminologia cultural reúne, pela primeira vez, dois dos maiores nomes da criminologia cultural, Jef Ferrell e Keith Hayward, com dois criminologistas culturais brasileiros, Salah H. Khaled Jr. e Álvaro Oxley da Rocha. A obra inaugura a coleção Crime, Cultura, Resistência e discute temas-chave como criminologia visual e a representação mediada do crime, criminalização cultural, protesto e política do espaço urbano, práticas subculturais, crime e música, etnografi a, criminologia antipositivista, e o nexo entre a segurança, opoliciamento e o controle social. Explorando a criminologia cultural é possível compreender um mundo contemporâneo no qual o crime e a imagem do crime espiralam juntos; onde as emoções do crime são construídas a partir de intensidades de experiência imediata, mas também do incessante luxo de fi lmes de crime e televisão criminal; onde a insegurança e o deslocamento defi nem tanto a vida pessoal quanto os contornos dos problemas sociais; onde as predações criminosas do capitalismo global, a violência generalizada delagrada nas populações de refugiados e migrantes, os perigos do terrorismo e os perigos das respostas a ele, as imagens e informações que circulam na mídia digital também merecem um olhar criminológico. Venha explorar conosco: uma nova era para a criminologia cultural no Brasil começa aqui.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.018 | 0.052 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".