Application of information communication technology (ICT) to legislative drafting: case studies of legislative drafting assistant softwares in Nigeria and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines the application of Information Communication Technology (ICT) tools by lawyers to simplify the task of legislative drafting of Bills and legislation. Using case studies in Nigeria and Canada some examples of the application of ICT to legislative drafting are examined. The idea of use and application of ICT tools for legislative drafting in Nigeria was first mooted in Nigeria in 1992 by the late Professor Keith Patchett during the Nigerian course in Legislative Drafting held in London. The lawyers that participated in the said course returned to Nigeria and trained other lawyers including Dr. Tonye Clinton Jaja, who led a team of computer experts to design a simple software for legislative drafting. Regarding Canada, Chantal Lamarre explains how the application of ICT for legislative drafting can improve the overall legislative process.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it