Proscription of terrorism in Nigeria: a comparative legal study
Bibliographic record
Abstract
Nigeria is bedeviled with an upsurge of terrorism. The country has adopted legislative measures in curbing the menace by enacting the Terrorism Prevention (Amendment) Act 2013.Proscription is relatively new to the Nigerian legal system; there is paucity of information on proscription in Nigeria. Proscription as contained in the Act remains controversial because it raises fundamental questions about fundamental rights and the limits of executive power. The Nigerian proscription regime do not operate in isolation, it is important to make comparisons in order to evaluate its effectiveness. In this article, it is observed that: The Nigerian regime on proscription is similar to that of Australia. Canada, the UK and the US; The enactment of antiterrorism law in these countries was catalyzed by an upsurge in terrorism; Proscription regimes often devolve wide discretions to the executive, with few effective checks and balances; In Nigeria, there is an absence of parliamentary debates as found in some countries; Outlawing groups who have divergent political ideologies or religious beliefs has serious implications for the individuals that are directly concerned and questions human rights principles. In conclusion, the proscription regime in Nigeria should seek to make a balance between security and fundamental rights of citizens.
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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 | 0.002 | 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 | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".