Bibliographic record
Abstract
Abstract Does austerity influence incumbent support? Existing studies struggle with conceptualizing the evolution of austerity's impact over time, estimating a causal effect, and analysing the reactions of different voters. This study theorizes that the effect of austerity on electoral preferences is not immediate, but gradual, as voters find out about the measures' consequences via the media. It leverages a survey in the field at the time of the austerity announcement in Romania in 2010, additional survey data collected immediately after this event and comprehensive daily media coverage to show that austerity measures do not have an immediate impact on incumbent support, anticipated turnout and expressing a vote preference. Instead, there is a gradual effect that is associated with increased media attention to budgetary cuts. This natural experiment allows the estimation of the immediate causal effect of austerity on electoral intentions. Difference‐in‐differences (DID) models show that the announcement triggered a massive loss of support for the incumbent among those who had voted for the party in power only a few months before. Austerity also led to the demobilization of the governing party's supporters. There is no evidence that those most directly affected by the spending cuts are more likely to punish the incumbent party.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".