Legal Issues Concerning Consumer Protection in Metaverse Operation in Nigeria: Taking a Leap from Uganda
Bibliographic record
Abstract
The Metaverse concept is a unique and trending digital platform, that has transformed commercial and social activities. Despite the prospect of Nigerian commercial activities in the Metaverse, it presents some unique challenges for consumers. Concerning this, the study examines the legal issues surrounding consumer protection in the Metaverse, to adopt possible legal ideas from Uganda’s experience, the study adopted a hybrid method of study, and 317 questionnaires were distributed to respondents residing within Nigeria. The results were analyzed with the aid of a descriptive and analytical approach. The study reveals that the metaverse concept provides significant prospects and potential for consumers. However, there are legal and social issues consumers may encounter. These challenges include a lack of legal measures in safeguarding consumer rights in the metaverse, the incidence of fraudulent activities by fraudsters, and several other challenges. It was therefore concluded and recommended that, for an effective operation of the metaverse that would not violate consumer rights, there is a need to incorporate an effective legal framework that will safeguard consumer interest. In this regard, the study recommends Uganda’s consumer protection laws as a model for the Nigerian government to adopt in safeguarding consumer commercial activities in the metaverse.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".