GSM and the Nigerian Economy: The Journey from 2004 to 2019
Bibliographic record
Abstract
The turn of the 21st century saw Nigeria liberalize its telecommunications sector with the deregulation of the industry, and the introduction of the Global System for Mobile communications (GSM) network platforms in the country. This move had an immediate positive socio-economic impact. Sectors like information technology, banking and finance, online trade, sporting, education, entertainment, security, and healthcare have significantly improved over the years. So far, tens of millions of direct employments have been directly provided via the platform. Furthermore, the country’s GDP attributable to telecommunication has constantly increased since the deregulation of the telecommunication industry. The paradigm shift has since seen the industry grow at a pace faster than most established networks in the world. However, several demerits have also stemmed from this advancement such as cyber-crime, cyber-bullying, blackmailing, and reduced productivity attributed to social media distractions. Even with the apparent progress, it can be concluded that the telecommunication sector is still quite underexploited in Nigeria. The lack of basic infrastructures like constant electricity and accessible road networks across several parts of the country, and the harsh economic policies have severely limited the potential for heightened economic productivity. 
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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 | 0.000 | 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) | 0.000 | 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".