Which Matters Most? Comparing the Impact of Issues and the Economy in American, British and Canadian Elections
Bibliographic record
Abstract
The objective of this study is to assess and compare the relative impact of issues and the economy on the vote in democratic elections. There is a rich and vast literature dealing with issue voting and an equally impressive literature concerning economic voting. For the most part, however, these amount to two separate streams of research. Relatively little attention has been paid to where these literatures overlap and less still to the simple but basic question: which matters most, the issues or the economy? The main debate in the issue voting literature recently has been between the directional and proximity models. That debate, engaging both technical and conceptual issues, has focused entirely on how issues play in an election, whether voters prefer the party that is closest to their own position or the party that is the strongest defender of their side on an issue. The question of how much issues affect the vote, however, has been neglected. Indeed, both the proximity and directional schools implicitly agree that issues matter, and so challenge the Michigan school's strong scepticism on the import of issues. Given that the difference between the two models is often quite small, a more fruitful line of investigation might be to return to the equally fundamental ‘how much’ question.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".