Do Voters Vote For Government Coalitions?
Bibliographic record
Abstract
In many countries, elections produce coalition governments. Downs points out that in such cases the rational voter needs to determine what coalitions are possible, i.e. to ascertain their probability and to anticipate the policy compromises that they entail. Downs adds that this may be too complex a task and concludes that ‘most voters do not vote as though elections were government-selection mechanisms’ (Downs, 1957: 300). We test Downs' ‘pessimistic’ conclusion in the case of the 2003 Israeli election, an election that was bound to produce a coalition government and in which the issue of what the possible coalitions were was at the forefront of the campaign. We show that voters' views about the coalitions that could be formed after the election had an independent effect on vote choice, over and above their views about the parties, the leaders and their ideological orientations. We estimate that for one voter out of ten, coalition preferences were a decisive consideration, that is, they induced the voter to support a party other than the most preferred one. For many others, they were a factor, though perhaps not the dominant one. Furthermore, the least informed were as prone to vote on the basis of coalition preferences as the most informed. Our evidence disconfirms Downs' pessimistic view that voters will decide not to care about the formation of government. When they are provided with sufficient information about the possible options, voters think ahead about the coalitions that may be formed after the election.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".