Centralized or decentralized personalization? Measuring intra-party competition in open and flexible list PR systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it