The Effects of US Presidential Elections on Work Engagement and Job Performance
Bibliographic record
Abstract
We predicted that presidential election results would spill over to influence the work domain. Individuals who voted for the winning candidate were expected to experience increased engagement, whereas individuals who voted for the losing candidate were expected to experience decreased engagement. We tested these predictions within the context of the 2016 US presidential election. Using a sample of 232 working Americans, work engagement and job performance were assessed one week prior to the election, the day after the election, and one week after the election. Contrary to our prediction, individuals who voted for Trump (the winning candidate) did not report increased work engagement, thereby providing no evidence of positive spillover. However, individuals who voted for Clinton (the losing candidate) were less engaged on the day after the election compared to baseline, demonstrating negative spillover. Downstream, work engagement was positively related to job performance. However, these effects were relatively short‐lived, as engagement returned to baseline levels within one week following the election. Our results suggest that elections can have important implications for work‐related outcomes. From a practical perspective we suggest that to the extent possible it may be prudent to avoid scheduling important work tasks for the days following presidential elections.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".