“Don’t worry, be happy (and the vote out the incumbent): economic anxiety and incumbent support”
Bibliographic record
Abstract
In recent years, there has been a substantial upsurge in the cost of living and income inequality across established democracies. These shifts, intensified by recent public health crises and growing political instability, have subjected voters to an extraordinary mix of social, political, and economic uncertainty. Despite widespread doubt about the future, a consistent finding in the economic voting literature highlights that voters’ evaluation of the past economy strongly influences their decision to either reward or penalize incumbents. Additionally, these studies find that voters’ forward-looking evaluations of personal economic conditions and general economic expectations often yield weak or negligible effects. These patterns motivate an important empirical question – why do future-oriented economic evaluations fall short in yielding a more substantial effect on incumbent support amid considerable personal struggle and uncertainty about economic conditions ahead? Grounded in appraisal theories of emotion, our study suggests that these consistent results may stem from existing measures inadequately capturing the multi-dimensional and affective nature of voters’ future economic concerns. Using data from the Canadian province of Ontario, we explore the extent of voters’ economic anxiety, identify the factors influencing these sentiments, and show that economic anxiety weakens support for incumbents in provincial and municipal elections.
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.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".