The Strategic Approach to Countering Cybercrime (SACC) framework: helping countries to tackle the growing threat to their economic and national security from cybercrime
Bibliographic record
Abstract
Cybercrime is a growing threat to global prosperity and national security around the world. Recent attacks against critical national interests in the US, Costa Rica and Ireland, among others, have shown the serious impact that malicious cyber activity can have. As the global threat of cybercrime continues to escalate in scale and complexity, both within and across borders, the need for countries to take a collaborative and strategic approach is clear. Countering cybercrime effectively requires a unified effort, with relevant stakeholders joining forces to assess the scale and nature of the threat, establish priorities, identify the optimal mix of interventions, evaluate the impact of adopted strategies, and adapt responses. This research paper presents a framework for a strategic approach to countering cybercrime – an adaptable toolkit designed to help national authorities and practitioners to meet the challenge.
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".