Estimating Causes of Cyber Crime: Evidence from Panel Data FGLS Estimator
Bibliographic record
Abstract
<em>This paper explores cyber crime from different perspectives, providing a deeper analysis of the phenomena itself as more sophisticated technical tools have emerged. Making detection of such methods quite difficult for law enforcement authorities around the globe, since the rise of the deep-web has made the prevention of such crimes is difficult if not impossible. However, the main objective of this paper is to examine the effect of unemployment rates and </em><em>GDP</em><em> growth per capita on the level of cyber-attacks in 10 countries (</em><em>USA</em><em>, </em><em>Belgium</em><em>, the </em><em>Netherlands</em><em>, </em><em>Japan</em><em>, </em><em>China</em><em>, </em><em>Italy</em><em>, </em><em>Spain</em><em>, </em><em>India</em><em>, and </em><em>Canada</em><em>) during the period of 2005-2017. In addition, this study seeks to provide insight into what might be the reason behind the fluctuating levels of attacks in these countries. This paper provides different authorities as well as the government with insight into how to take steps forward in preventing and combating these crimes.</em>
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How this classification was reachedexpand
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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; both teacher heads agree on what is shown here.
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".