Dynamics of Gubernatorial Approval: Evidence from a New Database
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This article introduces the State Executive Approval Database, a dataset of gubernatorial approval ratings that updates and adds to data previously collected by Beyle et al . In addition to the survey marginals, the dataset presents continuous quarterly and annual measures of the latent level of governor approval that are amenable for time series analysis. After evaluating how survey data availability varies across states and over time, I use the data to evaluate whether governors receive a honeymoon. While new governors do not have higher than expected levels of approval, the public expresses comparatively low levels of disapproval for new governors. This honeymoon is largely restricted to their first quarter in office and only occurs when they are elected to their first term. Governors who take office after their predecessor resigned get a slightly longer and more sustained reprieve from disapproval. Governor approval is also significantly shaped by unemployment levels in their state. These data will provide scholars with new opportunities to study accountability and representation at the state level.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it