Bibliographic record
Abstract
Advances in global telecommunication infrastructure, including computers, mobile phones, and the Internet, have brought about major transformation in world communication. In Nigeria, the young and the old now have access to the world from their homes, offices, cyber cafes and so on. Lately, internet or web-enabled phones and other devices like iPods, and Blackberry, have made internet access easier and faster. However, one of the fall outs of this unlimited access is the issue of cybercrime. Consequently, cybercrime, known as “Yahoo Yahoo” or “Yahoo Plus”, is a source of major concern to the country. Nigeria’s rising cybercrime profile may not come as a surprise, considering the high level of poverty and high unemployment rate in the country. What is surprising, however, is the fact that Nigerians are wallowing in poverty despite the huge human and material resources available in the country. With the aid of the human security approach, this paper aims to (i) establish a nexus between poverty and cybercrime in Nigeria; (ii) examine the efforts of the Nigerian government in forestalling cybercrime; and (iii) suggest measures that could be put in place to help in curbing cybercrime as well as bringing about poverty alleviation. The paper suggests that the government must put viable policies and programmes on poverty reduction and eradication in place. However, these policies and programmes need to be judiciously backed by actions.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".