Bibliographic record
Abstract
Following the development of international relations, lawyers and linguists have to face the diversity of law Systems and of languages, which is together a wealth and a drawback. This situation is an everyday 's reality in family law, business law and public law, as well in the European frame as in the world frame. Each language carries its own law concepts, whose pure equivalent are not always the apparently corresponding concepts in the other language. In a negotiation — in order to avoid any litigation resulting from a misunderstanding — or in a law suit — so that the dispute, subject matter of the trial or of the arbitration, is exactly appreciated by the judges or the arbitrators — the parties, their counsels, the judges, the arbitrators must know what is expressed, without focussing on the apparent meaning of the words, but knowing what they mean in each culture. In each situation, the exact meaning of the used tenus must be known, uppermost when they are translated. We must keep in mind that, when a language is spoken in several countries, the various national versions of this language are not always the same (the French language in France is not the same as in Belgium, Switzerland or Canada, the Germon language in Germany is not the same as in Switzerland or Austria). Moreover, the use of a third language, more particularly English, which is now the « international lingua franca » involves many risks, this language carrying the concepts of « common law » and having in addition different national versions, like « British English » and « American English ». Thanks to the « Babel of Laws and Languages » comparatists and linguists have a fine future.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.109 | 0.053 |
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".