Identifying and Explaining Career Patterns in Multi-level Democracies. A Comparative analysis of Catalonia, Quebec, Scotland and Wallonia
Bibliographic record
Abstract
With the processes of regionalization and Europeanization in formerly unitary democracies, there is a renewed interest for conceptual and empirical studies on political careers in multi-level systems. Not only in new federal political systems, but also in established federations. Yet, critical questions remain unsolved on both methodological and empirical issues. This paper seeks to provide original answers based on a comparative analysis of four regions from established and new federal systems: Catalonia in Spain, Quebec in Canada, Scotland in the UK and Wallonia in Belgium. The paper proceeds in two stages. From a methodological view, even though current studies analyse individual trajectories, they do not take individual careers but predominantly level-hopping movements as the unit of analysis. This paper demonstrates that an individual approach – following every single trajectory over time and across levels – is a better unit of analysis to uncover all career patterns. Based on a survival analysis of 2.443 careers, a quantitative analysis tests several hypotheses to explain variations in career patterns across regions. Two covariates of interest are more particularly tested: the effect of former regional/national experience and the differences of survival rates between regionalist and national political parties.
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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 | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".