Bibliographic record
Abstract
Abstract Popular criminology is a theoretical and conceptual approach within the field of criminology that is used to interrogate popular understandings of crime and criminal justice. In the last decade, popular criminology has most often been associated with analyses of fictional crime film, while in previous decades, popular criminology referred most often to “true crime” literature or, more generally, to popular ideas about crime and justice. While the term has appeared sporadically in the criminological literature for several decades, popular criminology is most closely associated with the work of American criminologist Nicole Rafter. Popular criminology refers in a broad sense to the ideas that ordinary people have about the causes, consequences, and remedies for crime, and the relationship of these ideas to academic discourses about crime. Criminologists who utilize this approach examine popular culture as a source of commonsense ideas and perceptions about crime and criminal justice. Films, television, the Internet, and literature about crime and criminal justice are common sources of popular criminology interrogated by criminologists working in this field. Popular criminology is an analytic tool that can be used to explore the emotional, psychological, and philosophical features of crime and criminal justice that find expression in popular culture. The popular cultural depiction of crime is taken seriously by criminologists working in this tradition and such depictions are placed alongside mainstream criminological theory in an effort to broaden the understanding of the impact of crime and criminal justice on the lives of everyday people. Popular criminology has become solidified as an approach within the broader cultural criminology movement which seeks to examine the interconnections between crime, culture and media.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.083 | 0.027 |
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".