Centralized or decentralized personalization? Measuring intra-party competition in open and flexible list PR systems
Bibliographic record
Abstract
This article offers a comparative analysis of electoral intra-party competition in four countries – Belgium, the Czech Republic, Finland and Luxembourg – based on an original data set of 79,621 candidates and 3150 party lists covering the last quarter century (1994–2017). We use two measures to describe the nature of intra-party competition over time, across countries and across party lists: a Gini coefficient and a measure of the effective number of candidates. First, in terms of change over time (personalization) – contra the presidentialization thesis – there is no concentration of intra-party competition around a few leaders over time. Second, in terms of the dynamics of concentration of votes (personalized politics), the results suggest a move beyond the clear-cut divide found in the literature between centralized and decentralized forms of personalized politics. Instead, personalized politics is best described by the concept of ‘elitization’, meaning the concentration of most votes on a medium-sized group of candidates (5–10 per lists). Finally, three sets of factors condition intra-party electoral competition: the electoral rules organizing preference votes, the level of elections (European, national and regional) and the presence on the party lists of incumbent politicians (party leaders, ministers and parliamentarians).
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".