Les droits des minorités et la répartition des compétences dans les Etats fédéraux : essai de synthèse au départ des expériences belge et canadienne
Bibliographic record
Abstract
This study focuses on the rights of linguistic and religious minorities in federal States.\nFirst, it begins with an exploration of the “internal” (i.e. the protection of fundamental rights in federal States) and “external” (i.e. the status of federal States in international (human rights) law) dimensions of the issue.\nThen, it analyses interactions between minority rights and the division of powers in these States. For this purpose, it relies on two complementary perspectives. The first one focuses on minority rights and shows that they constitute substantial limits to the federal and federate authorities’ exercise of powers. The second perspective concentrates on the constitutional norms dividing legislative powers and emphasizes that the protection of minority rights may represent either the stakes or an object of the division of powers. In the former case, the mere existence of such rights can explain particular aspects of the division of powers, while in the latter the Constitution expressly gives federal authorities the responsibility to protect certain rights.\nThe thesis underlines the various consequences of each of these configurations on the judicial interpretation of minority rights. In order to illustrate these observations, it mainly focuses on two legal orders: Belgium and Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".