Far away or yesterday? Shifting perceptions of time for political ends
Bibliographic record
Abstract
Voters evaluate political candidates not only based on their recent record but their history, often faced with weighing the relevance of long-past misdeeds in current appraisal. How should a distant transgression be taken to reflect on the present? Across multiple years, political figures and incidents, we found that people's subjective perceptions of time concerning political candidate's histories can differ radically, regardless of objective fact; political bias shapes people's perception of the time of things past. Results showed that despite equidistant calendar time, people subjectively view a favored politician's successes and opposing politician's failures as much closer in time, while a favored politician's failures and opponent's success seem much further away. Studies 1-3 tested the proposed phenomena across distinct (real and hypothetical) political contexts, while Study 4 tested the causal effects of temporal distance framing. Study 5 provided a final preregistered test of the findings. Overall, we demonstrate that partisans can protect their candidates and attack opponents by shifting their perception of time.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".