Bibliographic record
Abstract
En depit de fortes controverses politiques et d’une pratique parfois contradictoire, l’Amerique latine a la plus longue experience historique du federalisme moderne apres les Etats-Unis1. En 1811, le Venezuela est devenu le deuxieme pays moderne a adopter le federalisme. Le Mexique a suivi – de facon hesitante – en 1824, puis l’Argentine en 1853 et le Bresil en 1890. En depit d’autres transformations profondes dans leurs systemes politiques, ces quatre pays ont officiellement ete des federations de facon continue depuis la fin du 19e siecle2.Dans cet article, j’essaie de determiner si et comment cette longue experience a donne naissance a une conception strictement latino-americaine du federalisme. Je cherche aussi a depeindre la maniere dont les relations toujours compliquees entre la theorie et la pratique du federalisme se developpent en Amerique latine en vue de contribuer a notre comprehension plus large de ce phenomene.Alors que je me concentre sur les elements contemporains du federalisme latino-americain, il est important de garder a l’esprit que la comprehension regionale du federalisme est profondement influencee par son experience historique. Par ailleurs, l’existence de quatre federations en Amerique latine exige l’adoption d’une perspective comparative, tout en cherchant a presenter une vue d’ensemble. Puisqu’il est impossible de couvrir tous les aspects du federalisme latino-americain dans les limites de cet article, je prefere me concentrer sur trois dimensions
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".