Strategic Voting in the 2010 UK Election
Bibliographic record
Abstract
This paper analyzes strategic voting in the 2010 UK General Election using survey data from the 2010 British Election Study (BES). The paper consists of two parts. In the first part, we use survey responses to generate respondent-level preference orderings among the three major parties. Using these preference orderings, we simulate the 2010 election as if it were a national election using four different electoral systems: pairwise comparisons (in search of a Condorcet winner), the Borda count, the alternative vote, and Coombs’ method. We found that the Lib-Dems were the Condorcet winner and, as the compromise party, won under every tested method except the alternative vote (which they advocated). In the second part, we empirically test a rational-choice model of strategic voting. The model predicts that voters should take into accounted the expected utility of their vote and vote for their second-most preferred candidate if their first choice is clearly in third place in terms of likelihood of winning. The 2010 election offers a compelling case study because of the strong performance of the Lib-Dems, who traditionally finished a distant third. Our statistical analysis of the BES survey data provides support for our theoretical model of strategic voting.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".