Do coalition and formateur expectations affect vote switching?
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
Abstract The existing literature on vote switching – a major cause of electoral change – rarely discusses strategic incentives as motivating voters to switch parties between elections. We study how coalition-directed voting, a common type of strategic voting in parliamentary democracies, affects vote switching. Utilizing an original three-wave online panel survey conducted in Israel in 2019–2020, we show that voters engage in formateur optimization and policy balancing: they switch their vote in order to affect the identity of the next formateur and desert a party they previously voted for if they believe it will not enter the next coalition. We also show that the perceived level of competition between potential formateurs moderates the effect of coalition expectations on vote switching. The paper highlights the importance of coalition and formateur considerations in electoral change and contributes to a better understanding of both coalition-directed voting and individual-level vote switching.
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 | 0.004 | 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 | 0.002 | 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