Set on competing : contamination effects and parties' entry decisions in mass elections
Bibliographic record
Abstract
De acuerdo con las teorias Duvergerianas a largo plazo solo los partidos politicos viables deberian presentarse en solitario en las elecciones, mientras que los partidos no viables deberian crear coaliciones preelectorales o retirarse de la competicion. Sin embargo, en todas partes partidos politicos no viables continuan presentando candidaturas, lo que cuestiona las teorias Duvergerianas. Partiendo de esta paradoja, argumento que la superposicion de arenas electorales genera oportunidades para que partidos politicos viables en una arena se presenten en otras arenas donde no son viables. Mediante entrevistas en profundidad a lideres politicos del Canada y de Espana muestro como la superposicion de arenas electorales convierte la decision de presentar candidaturas cuando no se es viable en la estrategia dominante, mientras que crear coaliciones o retirarse de la competicion se convierten en alternativas sub-optimas. Esta situacion lleva a un exceso de oferta de partidos politicos compitiendo en comparacion con lo que las teorias de Duverger predicen. Mediante un analisis comparado con datos por 46 paises analizo los mecanismos institucionales y sociologicos que explican variacion en el numero de partidos politicos que compiten cuando no son viables.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 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".