‘Adequate protection’: an analysis of Nigeria’s data protection laws within an emerging global data protection framework
Bibliographic record
Abstract
The implementation of the European Union’s General Data Protection Regulation in 2018 appeared to be the catalyst for several countries to take data protection seriously, owing to concerns about transborder data flow restrictions, and has resulted in the global expansion of data protection laws. One of such countries is Nigeria, whose National Information Technology Development Agency (NITDA) introduced the Nigerian Data Protection Regulation (NDPR) in 2019. Nigerians are becoming more aware of the need to protect their personal data, and while the NDPR fulfils the need for a data protection law, it does not automatically mean that personal data of data subjects is adequately protected within Nigeria. Due to the lack of internationally binding data protection agreements, determining what constitutes an “adequate” data protection framework is challenging. The GDPR currently maintains the highest data protection standard and provides an assessment criterion in Article 45(2) for determining whether countries outside the EU have adequate data protection frameworks. In this regard, I assess how “adequate” Nigeria's data protection framework is in terms of the GDPR assessment criteria in Article 45(2). The Nigerian case is then compared to the Canadian data protection framework, which has received an adequacy decision from the EU. Based on this comparison, I make recommendations that, if implemented, will lay the groundwork for a data protection regime that meets the needs of Nigerian data subjects.
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".