Enhancing relationships between criminology and cybersecurity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it