Does Party Polarization Affect the Electoral Prospects of a New Centrist Candidate?
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
Does party polarization affect the electoral prospects of a new centrist candidate? The paper investigates this question in the context of a laboratory experiment where a centrist candidate is added to the race between a left candidate and a right candidate. The experimental design varies the polarization of the left and right candidates. The paper focuses on the effect of party polarization on the electoral prospects of a new centrist candidate through strategic voting behavior with experimental subjects acting as voters. The paper yields two main results: (1) party polarization initially improves the electoral prospects of a new centrist candidate; and (2) the effect of party polarization on the electoral prospects of the centrist weakens and ultimately disappears as elections are repeated. This happens because party polarization slows down the speed at which voters desert their candidate and vote strategically for the centrist in an apparent attempt at preventing the election of the candidate on the opposite side.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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 it