Bibliographic record
Abstract
Purpose This paper explores the various challenges associated with policing cybercrime, arguing that a failure to improve law enforcement responses to cybercrime may negatively impact their institutional legitimacy as reliable first responders. Further, the paper makes preliminary links between cybercrime and the paradigm of evidence-based policing (EBP), providing suggestions on how the paradigm can assist, develop, and improve a myriad of factors associated with policing cybercrime. Design/methodology/approach Three examples of prominent cybercrime incidents will be explored under the lens of institutional theory: the cyberextortion of Amanda Todd; the hacking of Ashley Madison; and the 2013 Target data breach. Findings EBP approaches to cybercrime can improve the effectiveness of existing and future approaches to cybercrime training, recruitment, as well as officers' preparedness and awareness of cybercrime. Research limitations/implications Future research will benefit from determining what types of training work at the local, state/provincial, and federal level, as well as evaluating both current and new cybercrime policing programs and strategies. Practical implications EBP approaches to cybercrime have the potential to improve police responses to cybercrime calls for service, save police resources, improve police–public relations during calls for service, and improve police legitimacy. Originality/value This paper links cybercrime policing to the paradigm of EBP, highlighting the need for evaluating and implementing effective evidence-based approaches to policing cybercrime.
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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 | 0.140 | 0.443 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.015 | 0.009 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".