Bibliographic record
Abstract
Face a la diversite des normes dans les differents pays membres, le legislateur OHADA dans sa politique de relancer les affaires et d’attirer les investisseurs a decide d'harmoniser les dispositions dans les 17 Etats membres. La realisation de ce projet imposait la mise en place de regles plus souples. Dans cette optique, le legislateur OHADA a permis a l'Etat et a ses demembrements de faire partie a une convention d'arbitrage. Cette harmonisation des normes juridiques se revele etre une grande reussite pour ces Etats de l'Afrique de l'ouest et a meme inspire certains pays. L'exemple du Canada en est une belle illustration. L'ouvrage traite l'arbitrage dans le systeme juridique OHADA, notament la possibilite pour les personnes publiques a soumettre leurs litiges a des arbitres. Dans ces pays les plus gros contrats sont conclus entre l'Etat et les investisseurs etrangers. Ce fut une nouveaute qui s'applique aussi bien au niveau interne qu'international contrairement a d'autres pays. En revanche, nous n'avons pas manque de soulever un probleme majeur lie a l'execution des sentences arbitrales par l'Etat mis en cause face au principe de souverainete des Etats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.003 |
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".