Bibliographic record
Abstract
Quebec is a jurisdiction of reference for Catalonia. The remarkable similarities between the two jurisdictions in terms of various scenarios and political realities account for their common inclinations. This interest translates into a close relationship on many levels, particularly in the academic sphere. Indeed, at a time when a Catalonian civil code is being considered, Quebec’s civil law solutions are perceived as special guidelines and are some of the materials most frequently consulted and examined at the preliminary stages of the Catalonian codification process. The first part of this article explores the reasons behind the complete codification of Catalonia’s civil matters at the dawning of the 21st century, by describing the legislative panorama of the difficult and unfinished history of codification in Spain. It also hightlights the challenges faced by Catalonia’s lawmakers in “developing” their legal system through a threestep process, namely: the Special Acts, the Sectorial Codes and Act 29/2002 of December 30th, as the first law of the Catalonian Civil Code. The second part of the article identifies certain concepts deeply imbued with Quebec civil law, demonstrating its unmistakable influence on the enactments concerning property, as well as those on trusts, legal entities and estates. It also includes a discussion of the project underway in the realm of family law. Finally, it contains a report on the law of obligations and contracts.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".