Intra-party Dynamics and Sub-National Influences in the Emergence of Party Splits in Brazil
Bibliographic record
Abstract
Party splits are one of the main sources of change within party systems. They occur when politicians, previously affiliated with another party, leave it and create a new party organisation. While several recent studies have focused on this topic, there remains a wealth of unexplored terrain about which conditions at the intra-party level can favour or hinder the emergence of party splits. This paper aims to investigate the emergence of party splits in Brazil, focusing on the role of intra-party competition in electoral contests for both federal and state legislatures. By analysing party-level data from Brazilian states between 1982 and 2022, this study demonstrates that the overall number of candidates competing within a party, particularly viable candidates with strong electoral prospects, increases the likelihood of party splits. The paper suggests that intra-party competition may be related to party splits by exacerbating dissatisfaction among political elites and/or exposing coordination challenges within the party.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".