Who governs? Analysis of the disputed effects of regionalism on legislative careers’ orientation in multilevel systems
Bibliographic record
Abstract
The processes of regionalization and federalization are unquestionable trends in Europe considering the flow of powers from states to subnational levels.In multi-level systems, the patterns of regional and national careers reflect this structural evolution.In the literature, two positions oppose each other about the effects of regionalism.Some authors argue that it does affect career patterns while other scholars found little evidence of the regionalism hypothesis.Unclear results in the literature are partly due to the limited number of comparative research across countries and across time, bias in case selection, and the choice of the unit of analysis.For the first time, this article aims to offer such analysis assessing empirically the regionalism hypothesis based on an original comparative dataset of 4.991 regional and national political careers in Belgium (Flanders, Wallonia, and Brussels), Canada (Ontario and Quebec), Spain (Catalonia and Castilla-La-Mancha), and the UK (Wales and Scotland).The intranational and international comparison of cases of strong and weak regionalism proves that regionalism does matter -regional politics attracts more professionalized MPs where regionalism is stronger -but the national parliament remains ultimately the most attractive political arena across regions.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".