Harnessing Big Data for Sustainable Development in Nigeria
Bibliographic record
Abstract
Nigeria faces a myriad of development challenges in her efforts to grow the economy, create jobs and achieve the Sustainable Development Goals by 2030. Since independence, the Government has developed many Plans and Strategies, including the current Economic Recovery and Growth Plan, in an attempt to address these challenges. The ERGP, which is broadly aligned to the SDGs, is aimed at improving macroeconomic stability; fostering economic growth and diversification; improving competitiveness; fostering social inclusion; and enhancing governance and security. Recent information, communication and technological advances have led to data -from both conventional and unconventional sources- to be readily available in high volumes and velocity and in a variety of forms, or simply, to a Data Revolution. This paper examines the role of Big Data and Data Revolution in promoting sustainable development in Nigeria, as well the emerging opportunities for Statisticians in this regard. The paper posits that the attainment of the SDGs will be greatly hampered if Statisticians do not ask the right questions; access relevant data information and, crucially, perform deeper analytics around data and information. Statisticians have an important role to play in promoting Nigeria’s sustainable development agenda, but only if they become more entrepreneurial; and adequately master and apply the requisite technical and non-technical skills.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".