Les expériences africaines de la diffusion libre du droit sur le Web : bilan et perspectives
Bibliographic record
Abstract
Face aux lacunes rencontrees dans les pays africains, correlativement a l’acces aux ressources juridiques, les diverses experiences de diffusion du droit via Internet offrent des perspectives particulierement interessantes de diffusion efficaces du droit. En effet, les efforts sud-africains, tanzaniens, burkinabes, beninois, pour ne citer que ces pays, proposent en ligne et gratuitement de la jurisprudence et de la legislation. Ces experiences encourageantes posent les jalons d’une expertise africaine en matiere de diffusion des droits africains, basee sur l’utilisation des technologies de l’information. Force est de constater que cette expertise se doit d’etre renforcee et consolidee par les divers acteurs de la diffusion en ligne des droits africains. Les juristes, universitaires et experts en technologies de l’information africains devraient egalement etre convies a developper et renforcer cette expertise, notamment par des echanges, des colloques, des partenariats regionaux et internationaux. La maitrise de la diffusion des ressources juridiques africaines, notamment par une utilisation judicieuse des technologies de l’information favoriserait la croissance de la culture juridique africaine et a la connaissance de celui-ci par la societe dans son ensemble. Cela participerait egalement a renforcer les liens entre la culture juridique originellement africaine et les cultures juridiques contemporaines.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".