Perceiver and target partisanship shift facial trustworthiness effects on likability
Bibliographic record
Abstract
The affective polarization characteristic of the United States' political climate contributes to pervasive intergroup tension. This tension polarizes basic aspects of person perception, such as face impressions. For instance, face impressions are polarized by partisanship disclosure such that people form positive and negative impressions of, respectively, shared and opposing partisan faces. How partisanship interacts with other facial cues affecting impressions remains unclear. Building on work showing that facial trustworthiness, a core dimension of face perception, is especially salient for ingroup members, we reasoned that shared and opposing partisanship may also affect the relation between facial trustworthiness characteristics and subsequent likability impressions. A stronger positive relation emerged for shared versus opposing partisan faces across more conservative and liberal perceivers (Experiments 1 and 2). Exploratory analyses showed that this difference links to perceived partisan threat (Experiment 1) and that experimentally manipulating inter-party threat strengthened opposing partisan derogation and shared partisan enhancement patterns (Experiment 2). These findings suggest that partisanship extends from affecting overall face impressions of partisans to affecting the relation between a core dimension of face perception and subsequent impressions. These findings highlight the prevalence of partisanship effects in basic aspects of person perception and have implications for intergroup behavior.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".