Subjective Well-Being Shapes U.S. Election Outcomes
Bibliographic record
Abstract
The current study broadly examines whether regional changes in subjective well-being (SWB) predict changes in U.S. presidential, Senate, and House of Representatives election outcomes. We will look at elections spanning 2010 to 2020 and use Gallup and BRFSS data, the latter of which is used to measure mental distress. We define non-incumbents as candidates in the non-incumbent party. We hypothesize that a decrease in SWB and an increase in mental distress over time will predict voting for non-incumbents and for the candidates in the minority party in the Senate and House of Representatives election. This study was initially pre-registered on February 25th, 2021. Since then, we have conducted analyses for the 2020 U.S. presidential and Senate elections at the MMSA level only. Elizabeth has written up a final paper with the most recent findings for her mini-thesis course. Files for the paper and our analyses are located on the pre-registration page. She delivered a virtual presentation in her mini-thesis course and at Victoria Research Day at the University of Toronto. Since the last pre-registration, we were made aware of the modifiable areal unit problem (MAUP) which refers to the possibility that results may not replicate when aggregating data to different geographical levels. As such, we are creating an updated pre-registration with this concern in mind and with the steps we will take to address it. Specifically, to address the MAUP, we will test whether the effects replicate at different levels of aggregation (e.g., county, district, MMSA).
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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 | 0.002 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".