Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The unprecedented spate of e-crime in Nigeria in recent times is quite alarming, and the negative impact on the socio-economy of the country is highly debilitating and of great concern. Numerous governmental and private initiatives have been deployed in order to curb and combat this menace. Recent reports revealed that there has not been significant reduction in this despicable art despite the various measures employed so far. Nigerian computer criminals are daily devising new ways of perpetrating this illegal trade and the existing methods of tracking these criminals are no longer suitable for to deal with their new tricks. The victims as well show increasing naivety and gullibility at the prospects incited by these fraudsters. This paper examines the trends, peculiarities, and reasons for the upsurge in ecrime in Nigeria. It further highlights these emerging tricks, possible infrastructures to be deployed for its treatment, and the implications of using such mechanisms.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it