Bibliographic record
Abstract
Purpose This paper seeks to examine certain important aspects of the domestic laws of Nigeria, the UK, the USA and Canada with a view to pointing out the remarkable differences capable of affecting adversely the war on terror. Design/methodology/approach Analyses the domestic laws of all four countries with a view to pointing out the remarkable differences capable of affecting adversely the war on terror. Findings Despite the obvious zeal and commitment with which nations and states of the world have set out to wage a legal war on terrorism, particularly the aspect of financing it, and despite the existence of a convention of which they are members, serious disparity exists in the legal frameworks adopted for the war. Even amongst such countries as the UK, the USA and Canada, uniformity of laws and approach is still a far‐fetched idea, a situation that is capable of hurting the international collaboration against terror. There is an urgent need for closer affinity between the laws of such countries while even countries like Nigeria that may not presently consider themselves as serious targets of terrorism need to urgently shirk themselves of such impressions and reform their laws. Originality/value The paper makes suggestions as to how the differences in the laws of the four countries may be corrected or down played, and how the international objectives of uniformity of laws may be achieved.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".