Politically Biased Time Perception and Perspective
Bibliographic record
Abstract
Abstract The main objective of this study was to determine if the estimation of time could be used to reveal an implicit political bias. The study also aimed at determining if a political bias is related to a specific temporal perspective or to other personality factors. The main demonstration is based on a bisection temporal task where the participants were asked to say if the duration of the presentation of a politician’s photo is short or long. There were three independent variables of interest: the location of politicians on the left (liberal) or right (conservative) on the political axis, the emotions expressed on a politician’s photo (joy, anger or neutral), and the political allegiances of the participants. Overall, compared to conditions with neutral faces or faces expressing joy, participants overestimated the duration of faces expressing anger. This effect, however, depends on the political allegiance of the participants. Compared to the neutral face condition, liberal participants overestimated the length of presentation of politicians’ faces in the joy and anger conditions. The results also showed that, compared to the condition in which photos of conservative politicians are presented, conservative participants underestimated the presentation duration of liberal politicians’ photos; such an influence of the orientation of presented politicians was not observed with liberal participants. The results also reveal that conservative participants differed from liberal participants on the future-oriented scale and on the past-positive-oriented scale of the Zimbardo Time Perspective Inventory (ZTPI). The study shows that time perception can be used to reveal a kind of implicit political bias.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".