An Assessment of the Impact of Municipal Laws on the Policing of Cybercrimes in Nigeria
Bibliographic record
Abstract
Abstract Internet crime can be defined as unlawful acts using the computer as either a tool or a target or both. Internet networks are used positively to conduct businesses, manage industrial and governmental activities, engage in personal communications, and conduct research. Also, certain confidential information is stored or passed through the medium of the internet. Credit cards containing information of users are used as the major means of buying and selling on the internet. Information infrastructure has become a critical part of the backbone of global economies; therefore, it is imperative that the general public be able to rely on the availability of such informational services with confidence that their communications and data are safe from unauthorized access or modification. It then becomes important for these and other information to be more secured. However, the speed of the internet, its affordability, and its elimination of distance make the internet the hotbed of crimes globally. Cybercrime growth has skyrocketed in recent times, especially in Nigeria; hence, the need for immediate action by law makers to stem the tide. This research, therefore, examines the adequacy or otherwise of the Nigerian legal framework in checking crimes being perpetrated using the internet as a platform with a view to making useful suggestions.
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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 | 0.000 |
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".