Bibliographic record
Abstract
The infusion of digital technology into contemporary society has had significant effects for everyday life and for everyday crimes. Digital Criminology: Crime and Justice in Digital Society is the first interdisciplinary scholarly investigation extending beyond traditional topics of cybercrime, policing and the law to consider the implications of digital society for public engagement with crime and justice movements. This book seeks to connect the disparate fields of criminology, sociology, legal studies, politics, media and cultural studies in the study of crime and justice. Drawing together intersecting conceptual frameworks, Digital Criminology examines conceptual, legal, political and cultural framings of crime, formal justice responses and informal citizen-led justice movements in our increasingly connected global and digital society. Building on case study examples from across Australia, Canada, Europe, China, the UK and the United States, Digital Criminology explores key questions including: What are the implications of an increasingly digital society for crime and justice? What effects will emergent technologies have for how we respond to crime and participate in crime debates? What will be the foundational shifts in criminological research and frameworks for understanding crime and justice in this technologically mediated context? What does it mean to be a ‘just’ digital citizen? How will digital communications and social networks enable new forms of justice and justice movements? Ultimately, the book advances the case for an emerging digital criminology: extending the practical and conceptual analyses of ‘cyber’ or ‘e’ crime beyond a focus foremost on the novelty, pathology and illegality of technology-enabled crimes, to understandings of online crime as inherently social. Twitter: @DigiCrimRMIT
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.087 | 0.034 |
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".