Enhancing relationships between criminology and cybersecurity
Bibliographic record
Abstract
‘Cybercrime’ is an umbrella concept used by criminologists to refer to traditional crimes that are enhanced via the use of networked technologies (i.e. cyber-enabled crimes) and newer forms of crime that would not exist without networked technologies (i.e. cyber-dependent crimes). Cybersecurity is similarly a very broad concept and diverse field of practice. For computer scientists, the term ‘cybersecurity’ typically refers to policies, processes and practices undertaken to protect data, networks and systems from unauthorised access. Cybersecurity is used in subnational, national and transnational contexts to capture an increasingly diverse array of threats. Increasingly, cybercrimes are presented as threats to cybersecurity, which explains why national security institutions are gradually becoming involved in cybercrime control and prevention activities. This paper argues that the fields of cyber-criminology and cybersecurity, which are segregated at the moment, are in much need of greater engagement and cross-fertilisation. We draw on concepts of ‘high’ and ‘low’ policing ( Brodeur, 2010 ) to suggest it would be useful to consider ‘crime’ and ‘security’ on the same continuum. This continuum has cybercrime at one end and cybersecurity at the other, with crime being more the domain of ‘low’ policing while security, as conceptualised in the context of specific cybersecurity projects, falls under the responsibility of ‘high’ policing institutions. This unifying approach helps us to explore the fuzzy relationship between cyber- crime and cyber- security and to call for more fruitful alliances between cybercrime and cybersecurity researchers.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".